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Record W1999886134 · doi:10.4141/p99-149

Herbicide-tolerant canola: weed control and yield comparisons in western Canada

2000· article· en· W1999886134 on OpenAlexaffvenueabout
K. Neil Harker, Robert E. Blackshaw, K. J. Kirkland, D. A. Derksen, David A. Wall

Bibliographic record

VenueCanadian Journal of Plant Science · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCanolaGlufosinateGlyphosateWeed controlWeedAgronomyBrassicaBiologyYield (engineering)

Abstract

fetched live from OpenAlex

Field experiments were conducted at five western Canadian locations in 1996 and 1997 to compare weed control and canola (Brassica napus L.) seed yields in the three major her-bicide-tolerant canola (HTC) systems. The main objective of this study was to determine if significant weed control and yield advantages could be expected by using herbicides "designated" for HTC compared to a more "standard" application of sethoxydim plus ethametsulfuron. In 3 of 10 site-years, glyphosate in Quest canola and imazethapyr/imazamox in 45A71 canola provided a yield advantage over the standard treatment. The yield advantages were restricted to the Lacombe and Lethbridge sites and ranged from 13 to 39% increases over the sethoxydim plus ethametsulfuron treatments. Among the HTC, weed control was usually greatest with glyphosate, followed by imazethapyr/imazamox, and then glufosinate. Glufosinate efficacy was often restricted due to advanced growth stages on some weeds. The standard treatment of sethoxydim plus ethametsulfuron did not provide better weed control than glyphosate, but in some cases did improve weed control compared to imazethapyr/imazamox or glufosinate. The HTC provide growers with new opportunities for the control of difficult weeds along with the option to employ in-crop herbicides with new modes of action in canola. Key words: Weed management, transgenic canola

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.253

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)0.0000.000

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 head0.013
GPT teacher head0.188
Teacher spread0.174 · 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 designObservational
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

Citations50
Published2000
Admission routes3
Has abstractyes

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