Evaluation of harvest aids application timing for lentil dry down
Bibliographic record
Abstract
Harvesting stage is a critical step for lentil producers to maintain high seed yield and good \nquality. Desiccating lentil with desiccants/harvest aids can dry down lentil evenly and quickly, \nand control late-growing green weeds, which enhances lentil harvest efficiency and allows early \nharvesting. Since the harvest aids are applied at a late growth stage, high herbicide residue in \nseeds may cause commercial issues with marketing lentil. Application timing of harvest aids is \ncritical for producers. Improper application timing may reduce yield and thousand seed weight, \nbut increase herbicide residue in seeds. Therefore, the objective of the harvest aids application \ntiming (% seed moisture) trial was to evaluate the responses of lentil to different herbicide \napplication timings at Saskatoon and Scott, Saskatchewan, over 2 years (2012 and 2013). For \nthis trial, glyphosate (900 g a.e. ha-1), saflufenacil (50 g a.i. ha-1), and the combination of \nglyphosate plus saflufenacil (900 g a.e. ha-1 and 36 g a.i. ha-1) were applied when seed moisture \ncontent was 60%, 50%, 40%, 30% and 20%. Apart from these herbicide treatments, there was \nalso an untreated control, which is desiccated naturally. Significant relationships between \nevaluated variables and application timing on the basis of seed moisture content were detected. \nAlso, this trial indicated that early application timing (60% application seed moisture) could \nresult in reductions in lentil yield and thousand seed weight. Glyphosate residue in seeds was less \nthan 4 mg kg-1 when glyphosate was applied alone at 30% and 20% average seed moisture. \nGlyphosate residue decreased when adding saflufenacil to glyphosate. Saflufenacil residue \nconsistently increased with earlier application timing of the harvest aids.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".