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Record W2090427642 · doi:10.7202/706073ar

Herbicide-resistant crops : A weed scientist’s perspective

2005· article· en· W2090427642 on OpenAlexaffvenue
W.E. Dyer

Bibliographic record

VenuePhytoprotection · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHerbicide resistanceBiologyWeedResistance (ecology)Weed controlAgronomyCultivarBiotechnologyGlyphosate

Abstract

fetched live from OpenAlex

Herbicide-resistant crops offer a potentially valuable alternative strategy for weed management. If used appropriately, they may promote the use of agrichemicals more environmentally benign than the herbicides they replace, and provide producers with additional tools for controlling weeds. However, the controversy surrounding the development and use of these cultivars may limit and eventually prevent their widespread adoption. Concerns include: overuse of herbicides, escape of herbicide resistance genes from resistant cultivars into weedy relatives, genetic modifications for resistance conferring weediness to the cultivar (i.e. volunteer plants in subsequent crops), potential pleiotropic effects of genetic modifications for resistance, and selection of new herbicide-resistant weeds in the new herbicide regime. Of these concerns, the potential for selecting new resistant weeds may have the highest likelihood of affecting the long-term success of herbicide-resistant crops.

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)0.009
metaresearch head score (Gemma)0.005
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.011
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0040.008
Open science0.0020.002
Research integrity0.0110.010
Insufficient payload (model declined to judge)0.0050.002

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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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
GenreCommentary

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

Citations6
Published2005
Admission routes2
Has abstractyes

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