replicate+test

[ˈsɑ:mpl]

adequate sample образец, соответствующий требованиям biased sample стат. необъективная выборка biased sample стат. пристрастная выборка biased sample смещенная выборка biased sample стат. смещенная выборка bivariate sample двумерная выборка blood sample образец крови sample образец, образчик; book of samples альбом образцов censored sample цензурированная выборка cluster sample стат. групповой выбор commercial sample (not for sale) торговый образец товара не для продажи counter sample конкурирующий образец free sample бесплатный образец judgment sample преднамеренный выбор large sample большая выборка lot sample выборка из партии moderate-sized sample выборка умеренного объема multicensored sample многократно цензурированная выборка multiphase sample многофазная выборка multipurpose sample многоцелевая выборка multistage sample многоступенчатая выборка nongrouped sample негруппированная выборка normal sample нормальная выборка ordered sample упорядоченная выборка probability sample вероятностная выборка proportionate sample пропорциональная выборка purposive sample преднамеренная выборка quality sample выборочный уровень качества quasi-random sample квазислучайная выборка quota sample пропорциональная выборка quota sample стат. пропорциональная выборка random sample образец, взятый по схеме случайного отбора random sample произвольная выборка random sample случайная выборка random sample случайный отбор reference sample контрольный образец replicate sample повторная выборка representative sample представительная выборка representative sample репрезентативная выборка sample брать пробы sample выборка sample выборочная партия (товара, изделий) sample выборочная партия sample stat. выборочная совокупность sample замер sample образец, образчик; book of samples альбом образцов sample образец sample образец товара sample образчик sample отбирать образцы, брать образчик или пробу sample отбирать образцы sample отбирать образцы или пробы sample проба sample пробовать, испытывать sample производить выборку sample шаблон, модель sample шаблон sample of data вчт. набор данных sample of no value stat. непредставительная выборка sample of no value stat. нерепрезентативная выборка single sample однократная выборка singly censored sample однократно цензурированная выборка small sample малая выборка small sample theory теория малых выборок stratified sample районированная выборка stratified sample расслоенная выборка stratified sample типическая выборка systematic sample систематическая выборка systematical sample систематическая выборка test sample образец для испытаний test sample опытный образец test sample пробный образец three-stage sample трехступенчатая выборка trade sample образец товара truncated sample усеченная выборка two-stage sample двухступенчатая выборка unbiased sample беспристрастная выборка unbiased sample несмещенная выборка unbiased sample объективная выборка uncensored sample нецензурированная выборка unordered sample неупорядоченная выборка unrepresentative sample непредставительная выборка unrepresentative sample нерепрезентативная выборка unsolicited sample образец, высланный без запроса

   1) Programs and the Complexity of Human Mental Processes

   In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)

   2) Artificial Intelligence Is an Engineering Discipline

   Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)

   3) A Sceptical View of Artificial Intelligence

   Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....

   When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)

   4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, Eventually

   Just as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)

   5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the Program

   Many problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)

   6) The Meaning of a Symbolic Description

   What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)

   7) The Principle of Artificial Intelligence

   [AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)

   8) Artificial Intelligence Recognizes the Need for Knowledge in Its Systems

   The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)

   9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract Form

   The basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)

    10) There Are Many Types of Reasoning

   There are many different kinds of reasoning one might imagine:

    Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."

    Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)

    11) Programs Are Beginning to Do Things That Critics Have Asserted to Be Impossible

   Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)

    12) The Synthesis of Man and Machine

   Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)

    13) The Thesis of Good Old-Fashioned Artificial Intelligence (GOFAI)

   The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)

    14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory Formation

   It is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological AI

   Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.

   ♦ Weak Psychological AI

   Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.

   ♦ Strong Psychological AI

... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)

   ♦ Suprapsychological AI

   Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)

    16) Determination of Relevance of Rules in Particular Contexts

   Even if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)

    17) Form and Content Are Not Fundamentally Different

   Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)

    18) The Assumption That the Mind Is a Formal System

   Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)

    19) A Statement of the Primary and Secondary Purposes of Artificial Intelligence

   The primary goal of Artificial Intelligence is to make machines smarter.

   The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)

    20) Mathematical Logic Provides the Basis for Theory in AI

   The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....

   AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)

    21) Perceptual Structures Can Be Represented as Lists of Elementary Propositions

   In artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)

    22) Definitions of Artificial Intelligence

   Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)

    23) The Computer Can Not Be a Model of the Mind

   Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)

    24) Computers Will Not Always Be Inferior to Human Brains

   The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)

1) оператор, знак операции

см. тж. crossover operator, data parallel operator, decrement operator, dereferencing operator, dot operator, equality test operator, infix notation, modulus operator, nullary operator, operand, operator associativity, operator overloading, output operator, plus-plus operator, precedence, postfix operator, prefix notation, primitive, proximity operator, remainder operator, replicate operator, right-associative operator, string operator, subscript operator

2) см. telecoms operator

3) оператор ( ЭВМ, вычислительного комплекса, центра обработки данных)

а) оператор ЭВМ - почти исчезнувшая специальность, бывшая популярной во времена пакетной обработки. Оператор устанавливал (монтировал) магнитные диски и ленты, заправлял бумагу в АЦПУ, ставил на счёт колоды перфокарт с заданиями и т. д. Например, without operator intervention - без вмешательства оператора

см. тж. mainframe

б) обязанности оператора вычислительного комплекса и центра обработки данных простираются от обслуживания оборудования до выполнения специализированных запросов

см. тж. human operator

1) образец; проба

2) кернер

3) отбирать образцы

•

- audit sample

* * *

1.   образец, проба

2.   выборка

3.   шаблон; модель

4.   керн

- conforming to the sample

- check sample

- dip sample

1) опыт || производить опыты

2) эксперимент || экспериментировать

•

- auxiliary experiment

- balanced experiment

- binary experiment

- binomial experiment

- bivariate experiment

- branching experiment

- certain experiment

- check experiment

- combined experiment

- comparative experiment

- complete experiment

- complex experiment

- conditional experiment

- confounded experiment

- control experiment

- crossover experiment

- crucial experiment

- decision experiment

- designed experiment

- diagnostic experiment

- disarranged experiment

- discriminating experiment

- dosage-mortality experiment

- dummy experiment

- eighth-replicated experiment

- eliminative experiment

- extremal experiment

- factorial experiment

- field experiment

- game experiment

- half-replicated experiment

- heuristic experiment

- hierarchical experiment

- hypothetical experiment

- idealized experiment

- incomplete experiment

- lattice experiment

- linear experiment

- matching experiment

- model experiment

- multifactorial experiment

- multiple experiment

- multiresponse experiment

- multivariate experiment

- needle experiment

- nested experiment

- nonfactorial experiment

- nonreplicated experiment

- nonstatistical experiment

- numerical experiment

- optimal experiment

- paired experiment

- paired-comparison experiment

- patterned experiment

- probabilistic experiment

- psi experiment

- quantitative experiment

- ramified experiment

- random experiment

- randomized experiment

- ranking experiment

- reference experiment

- regression experiment

- repetitive experiment

- replicate experiment

- replicated-free experiment

- restricted experiment

- rotation experiment

- sampling experiment

- screening experiment

- sensitivity experiment

- sequential experiment

- significant experiment

- simple experiment

- simulated experiment

- simulation experiment

- single experiment

- singular experiment

- sufficient experiment

- symmetric experiment

- test experiment

- two-level experiment

- two-sample experiment

- unbalanced experiment

- unconditional experiment

- undesigned experiment

- unit experiment

- univariate experiment

- unplanned experiment

- unramified experiment

- up-and-down experiment

- well-planned experiment

culture 1. культура (напр. бактерий) ; 2. разведение, выращивание

culture культура, культивирование организмов или тканей в лабораторных условиях на искусственно приготовленной среде

culture chamber культуральная камера

adhesive culture культура в капле среды на покровном стекле

aerated culture аэрированная культура

aerobic culture аэробная культура

agar culture культура на агаре

agitated culture перемешиваемая встряхиванием культура

anaerobic culture анаэробная культура

animal culture культура клеток животных

aroma-producing culture ароматообразующая культура

artificial culture искусственное разведение

aseptic culture асептическое выращивание

axenic culture аксенная культура, стерильная культура; чистая культура

batch culture культура одного производственного цикла

batch culture периодическая культура

broth culture бульонная культура

callus tissue culture культура каллусных тканей

cell culture культура клеток

cell suspension culture суспензионная культура клеток

chorioallantoic culture хориоаллантоисная культура

clonal culture клонированная культура

contaminated culture загрязненная культура

contaminated culture нечистая культура

continuons-flow culture проточная культура

control culture контрольная культура

dark-grown culture выращенная в темноте культура

deep-liquid culture глубинная культура

differentiated culture дифференцированная культура

diploidization of culture диплоидизация культуры

dried culture высушенная культура

droplet culture капельная культура

embryo culture культура эмбрионов

enrichment culture обогатительная культура

excised embryo culture культура изолированных эмбрионов

excised organ culture культура изолированных органов

experimental culture экспериментальная культура

explant culture культура ткани

exposition of culture воздействие на культуру

fed-batch culture подпитываемая культура одного производственного цикла

freeze-dried culture лиофилизованная культура

frozen culture замороженная культура

fungal culture культура грибов

germ culture микробная культура

glycerolized culture глицериновая культура

growing culture растущая культура; размножающаяся культура

habituated culture адаптированная культура

heavily sporulating culture обильно спорулирующая культура

heterogeneous culture гетерогенная культура

high density culture концентрированная культура

homogeneous culture гомогенная культура

housing patent culture коллекционная патентованная культура

human cell culture культура клеток человека

human tissue culture культура ткани человека

improving culture conditions оптимизированные условия культивирования

impure culture загрязнённая культура

isolated clonal culture изолированная клонированная культура

isolated protoplast culture культура изолированных протопластов

laboratory culture лабораторная культура

large-scale culture крупномасштабная культура

liquid culture культура в жидкой среде

logarithmic phase culture культура, находящаяся в логарифмической фазе роста

long-period culture длительная культура

maintaining culture поддерживаемая культура

maintaining culture сохраняемая культура

manufacturing plant culture промышленная культура

mass culture массовая культура

microcarrier culture культура клеток на микроносителях

mixed culture смешанная культура

monolayer cell culture монослойная культура

monolayer culture монослойная культура, однослойная культура

monospecies culture монокультура

monoxenic culture моноксенная культура

negative culture отрицательная культура

nonproliferating culture непролиферирующая культура

nonsporulating culture неспорообразующая культура

old culture старая культура

open culture непрерывная культура

overnight culture ночная культура

Petri dish culture культура на чашках Петри

Petry dish culture культура на чашках Петри

plant cell culture культура клеток растения

plant tissue culture культура клеток растительной ткани

plate culture культура на чашках Петри

positive culture положительная культура

preserved culture законсервированная культура

preserved culture сохраняемая культура

primary culture первичная культура

production culture производственная культура

prompt culture закваска

prompt culture затравочная культура

protoplast culture культура протопластов

pure culture чистая культура

reference culture тест-культура

replicate culture реплицированная культура

resistant culture резистентная культура

roll bottle culture роллерная культура

rotated culture роллерная культура

routine culture стандартная культура

seed culture посевная культура

selective culture селективная культура

serum-free culture бессывороточная культура

short-term culture кратковременная культура

single cell culture культура, полученная из одной клетки

single cell culture культура одной клетки

single cell culture культура одноклеточного организма

single-cell culture культура, выделенная из одной клетки

sister culture сестринская культура

slant culture культура на скошенном агаре

slide culture культура на предметном стекле

slope culture культура на скошенном агаре

smear culture культура мазком

soil culture почвенная культура

soil-water culture культура на почвенной вытяжке

spent culture отработанная культура

sporulating culture спорулирующая культура

stabilized culture стабилизированная культура

starter culture закваска

static culture статическая культура

steady-state culture стационарная культура

stock culture штамм, чистая культура

stock culture collection базовая коллекция культур микроорганизмов

stock yeast culture маточные дрожжи

stored culture законсервированная культура

stroke culture поверхностная культура

submerged culture погружённая культура, глубинная культура

surface culture поверхностная культура

suspended cell culture культура ткани из суспендированных клеток

suspension culture суспензионная культура

synchronized culture синхронизированная культура

synchronous culture синхронно растущая культура

test-tube culture пробирочная культура

tissue culture тканевая культура, культура ткани

tumor tissue culture культура опухолевой ткани

two-membered culture смешанная культура двух видов организмов

type culture стандартная культура

type culture типовая культура

unialgal culture альгологически чистая культура (свободная от водорослей других видов)

water culture водная культура

working culture рабочая культура

young culture молодая культура

 

репетиция
Пошаговая отработка в реальном масштабе времени определенных видов деятельности с привлечением всех участвующих в ней сотрудников. Репетиции проводятся на объектах Игр или в других местах, и воспроизводят типичную деятельность во время Игр, осуществляемую в определенный промежуток времени. Репетиции могут охватывать проведение церемоний и спортивных соревнований или обеспечивать проверку технологической готовности. Репетиции проводятся до тестовых мероприятий и до начала Игр. Цель проведения репетиций — отработать одновременную совместную деятельность функциональных подразделений, команд, обслуживающих объекты, и группы управления, и выявить системные или общие проблемы.
[Департамент лингвистических услуг Оргкомитета «Сочи 2014». Глоссарий терминов]

EN

rehearsal
Real time walkthroughs of specific activities involving the entire relevant workforce. They are conducted on site or off site and replicate a typical time period of the Games. There are a number of types of rehearsals (e.g. technology, ceremonies and sport). Rehearsals are conducted prior to test events and prior to the Games. The intention of a rehearsal is to provide functional teams, venue teams and command teams with simultaneous activities as a means of assessing systemic or system-wide issues.
[Департамент лингвистических услуг Оргкомитета «Сочи 2014». Глоссарий терминов]

Тематики

• спорт (управление Играми)

EN

• rehearsal