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Record W1606247637

The Effect of Plants with Novel Traits (PNT) Regulation on Mutation Breeding in Canada

2009· article· en· W1606247637 on OpenAlexaffabout
G. G. Rowland

Bibliographic record

VenueInduced plant mutations in the genomics era. Proceedings of an International Joint FAO/IAEA Symposium, 2008 · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGovernment (linguistics)GermplasmNoveltyTraitAgency (philosophy)BiotechnologyAgriculturePolitical sciencePlant breedingProduct (mathematics)BiologyGeographyEcologyAgronomySociologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

The Canadian Environmental Protection Act (1988) has within it a definition for biotechnology. This definition would have allowed the government department, Environment Canada, to regulate all genetically modified organisms (GMOs) in Canada. In response to this, the Canadian Food Inspection Agency (CFIA), which reports to the Minister of Agriculture and Agri-Food Canada, developed the concept of a Plant with Novel Trait (PNT). Not only does this definition capture GMOs, it also includes induced mutations, natural mutations and exotic germplasm that have not previously been grown in Canada. It is a system that is product, not process based. However, apart from questions regarding the novelty of traits in new plant varieties, breeders are asked by CFIA to identify the process used to develop the trait or traits in question. Field trials involving breeding lines with a PNT may be subject to confined testing. This conference celebrated 80 years of unconfined development and testing of induced plant mutations. This regulation is time consuming, expensive and an innovation barrier for Canadian plant breeding. It can only be hoped that other nations, and particularly those that have successfully used induced mutations, will not emulate Canada’s approach.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.224
Teacher spread0.205 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueInduced plant mutations in the genomics era. Proceedings of an International Joint FAO/IAEA Symposium, 2008Same topicGenetically Modified Organisms ResearchFrench-language works237,207