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 quality. Desiccating lentil with desiccants/harvest aids can dry down lentil evenly and quickly, and control late-growing green weeds, which enhances lentil harvest efficiency and allows early harvesting. Since the harvest aids are applied at a late growth stage, high herbicide residue in seeds may cause commercial issues with marketing lentil. Application timing of harvest aids is critical for producers. Improper application timing may reduce yield and thousand seed weight, but increase herbicide residue in seeds. Therefore, the objective of the harvest aids application timing (% seed moisture) trial was to evaluate the responses of lentil to different herbicide application timings at Saskatoon and Scott, Saskatchewan, over 2 years (2012 and 2013). For this trial, glyphosate (900 g a.e. ha-1), saflufenacil (50 g a.i. ha-1), and the combination of glyphosate plus saflufenacil (900 g a.e. ha-1 and 36 g a.i. ha-1) were applied when seed moisture content was 60%, 50%, 40%, 30% and 20%. Apart from these herbicide treatments, there was also an untreated control, which is desiccated naturally. Significant relationships between evaluated variables and application timing on the basis of seed moisture content were detected. Also, this trial indicated that early application timing (60% application seed moisture) could result in reductions in lentil yield and thousand seed weight. Glyphosate residue in seeds was less than 4 mg kg-1 when glyphosate was applied alone at 30% and 20% average seed moisture. Glyphosate residue decreased when adding saflufenacil to glyphosate. Saflufenacil residue consistently increased with earlier application timing of the harvest aids.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".