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Record W2079801504 · doi:10.4141/p99-063

Effect of preharvest applications of glyphosate on the drying, yield and quality of canola

2000· article· en· W2079801504 on OpenAlexvenueaboutno aff
A. L. Darwent, K. J. Kirkland, L. Townley‐Smith, K. Neil Harker, Allan J. Cessna

Bibliographic record

VenueCanadian Journal of Plant Science · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsCanolaPreharvestGlyphosateGerminationAgronomyBrassicaBrassica rapaBiologyYield (engineering)HorticulturePostharvest

Abstract

fetched live from OpenAlex

Potential use of preharvest applications of glyphosate in canola (Brassica rapa L.) in western Canada both as a harvest aid and to control weeds prompted this study. Experiments were conducted from 1988 to 1990 at four locations in the region to document the effectiveness of preharvest applications of glyphosate to enhance canola seed and foliage drydown and to measure the effect of such applications on seed yield and quality. Glyphosate was applied in early August to early September at rates of 0.45, 0.90 and 1.70 kg acid equivalent ha −1 to canola with seed moisture contents ranging from 79 to 12%. Seed and foliage moisture drydown were not enhanced by glyphosate treatment. Glyphosate treatments generally had little or no effect on canola seed yields, seed weight, seed germination, green seed content or oil content except when applied when the pods were green and when seed moisture contents were high. Seed/pod moisture was not a reliable indicator of stage of canola development and thus, should not be used alone as an indicator of when preharvest applications of glyphosate can be applied to canola without decreasing seed yield and quality. Key words: Glyphosate, canola, Brassica rapa L., preharvest application, desiccation, seed quality

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)0.002
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 score0.631
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.034
GPT teacher head0.264
Teacher spread0.230 · 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

Citations25
Published2000
Admission routes2
Has abstractyes

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