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Record W2099660760 · doi:10.7202/706074ar

Herbicide-resistant crops in resistant weed management : An industrial perspective

2005· article· en· W2099660760 on OpenAlexaffvenue
Dale L. Shaner

Bibliographic record

VenuePhytoprotection · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsWeed controlHerbicide resistanceWeedResistance (ecology)AgronomyBiologyCropGlyphosateAgricultureAgroforestryBiotechnologyBusinessEcology

Abstract

fetched live from OpenAlex

Some of the first products of biotechnology to reach the marketplace have been herbicide-resistant crops. Industry sees the development of herbicide-resistant varieties as a way to increase the availability of proven herbicides for a broader range of crops. However, the development of herbicide- resistant crops requires special attention to potential environmental questions such as herbicide usage, selection of resistant weed biotypes and spread of resistance from the resistant crop to wild species. Industry is actively addressing these concerns during the process of development. Proper development and use of herbicide-resistant crops in integrated weed management programs will provide farmers with increased flexibility, efficiency, and decreased cost in their weed control practices without increasing the risk of herbicide-resistant weeds. Furthermore, herbicide-resistant crops should prove to be valuable tools in managing herbicide- resistant weeds.

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)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.028
GPT teacher head0.244
Teacher spread0.217 · 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
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

Citations5
Published2005
Admission routes2
Has abstractyes

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