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Record W3000665059 · doi:10.1201/b15412-19

GENETICALLY MODIFIED ORGANISMS IN ENVIRONMENT

2010· book-chapter· en· W3000665059 on OpenAlexaboutno aff

Bibliographic record

VenueEnvironmental Biotechnology · 2010
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicEnvironmental and Biological Research in Conflict Zones
Canadian institutionsnot available
Fundersnot available
KeywordsGenetically modified organismBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Biotechnology and genetic modifications are commonly used as interchangeable. Genetically modified is a special set of technologies that alter the genetic makeup of such living organisms as animals, plants or bacteria. Combining genes from different organisms is known as recombinant DNA technology and the resulting organism is said to be ‘genetically modified’, ‘genetically engineered’, or ‘transgenic’. Locating genes for important traits such as those conferring insect resistance or desired nutrients is one of the most limiting steps in the process. However, genome sequencing and discovery programmes for hundreds of different organisms are generating detailed maps along with dataanalyzing technologies to understand and use them. Genetically modified organism (GMO) is used for producing genetically modified (GM) foods. GM foods have been available since 1990s. The most common modified foods are derived from plants: soyabean, corn, canola and cotton seed oil and wheat (ISAAA, 2002).The process of producing GMO used for GM foods may involve taking DNA from one organism, modifying it in a laboratory and then inserting it into the target organism’s genome to produce new and useful traits or phenotypes. Such GMOs are generally referred to as transgenic. Other methods of producing a GMO includes increasing or decreasing the number of copies of a gene already present in the target organism, silencing or removing a particular gene or modifying the position of a gene within the genome. In 2006, a total of 252 million acres of transgenic crops were planted in 22 countries by 10.3 million farmers. The majority of these crops were herbicide and insect resistant, soyabeans, corns, cotton, canola and alfalfa. Other crops grown commercially or field tested are a sweet potato resistant to a virus that could decimate most of the African harvest, rice with increased iron and vitamins that may alleviate chronic malnutrition in Asian countries and a variety of plants able to survive weather extremes in the horizon are bananas that produce human vaccines against infectious diseases such as hepatitis B, fish that mature more quickly, cows that are resistant to bovine spongiform encephalopathy (mad cow disease); fruit and nut trees that yield years earlier and plants that produce new plastics with unique properties. In 2006, countries that grew 97% of the global transgenic crops were the United States (53%), Argentina (17%), Brazil (11%), Canada (6%), India (4%), China 3%, Paraguay (2%) and South Africa (1%). Although growth is expected to plateau in industrialized countries, it is increasing in developing countries. The next decade will see exponential progress in GM product development as researchers gain increasing and unprecedented access to genomic resources that are applicable to organism beyond the scope of individual projects. Genetic engineering may accelerate the damaging effects of agriculture, have the same impact as conventional agriculture, or contribute to more sustainable agricultural practices and the conservation of natural resources, including biodiversity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.030
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.007
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0300.011

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.206
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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