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Record W2549631677 · doi:10.1080/21645698.2016.1257468

Canadian regulatory perspectives on genome engineered crops

2016· article· en· W2549631677 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGM crops & food · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBiotechnologyAgricultureEmerging technologiesCommercializationEuropean unionAgricultural biotechnologyProduct (mathematics)BusinessBiologyComputer scienceInternational tradeMarketingEcology

Abstract

fetched live from OpenAlex

New breeding techniques in plant agriculture exploded upon the scene about two years ago, in 2014. While these innovative plant breeding techniques, soon to be led by CRISPR/Cas9, initially appear to hold tremendous promise for plant breeding, if not a revolution for the industry, the question of how the products of these technologies will be regulated is rapidly becoming a key aspect of the technology's future potential. Regulation of innovative technologies and products has always lagged that of the science, but in the past decade, regulatory systems in many jurisdictions have become gridlocked as they try to regulate genetically modified (GM) crops. This regulatory incapability to efficiently assess and approve innovative new agricultural products is particularly important for new plant breeding techniques as if these techniques are classified as genetically modified breeding techniques, then their acceptance and future will diminish considerably as they will be rejected by the European Union. Conversely, if the techniques are accepted as conventional plant breeding, then the future is blindingly bright. This article examines the international debate about the regulation of new plant breeding techniques and then assesses how the Canadian regulatory system has approached the regulation of these technologies through two more public product approvals, GM apples and GM potatoes, then discusses other crop variety approval and those in the regulatory pipeline.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.946
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.196
Teacher spread0.180 · 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