Evaluation of Harvest-Aid Herbicides as Desiccants in Lentil Production
Bibliographic record
Abstract
Desiccants are currently used to improve lentil dry-down prior to harvest. Applying desiccants at growth stages prior to maturity may result in reduced crop yield and quality, and leave unacceptable herbicide residues in seeds. There is little information on whether various herbicides applied alone or as a tank-mix with glyphosate have an effect on glyphosate residues in harvested seed. Field trials were conducted at Saskatoon and Scott, Saskatchewan, Canada, from 2012 to 2014 to determine whether additional desiccants applied alone or tank mixed with glyphosate improve crop desiccation and reduce the potential for unacceptable glyphosate residue in seed. Glufosinate and diquat tank mixed with glyphosate were the most consistent desiccants, providing optimal crop dry-down and a general reduction in glyphosate seed residues without adverse effects on seed yield and weight. Saflufenacil provided good crop desiccation without yield loss, but failed to reduce glyphosate seed residues consistently. Pyraflufen-ethyl and flumioxazin applied alone or tank mixed with glyphosate were found to be inferior options for growers as they exhibited slow and incomplete crop desiccation, and did not decrease glyphosate seed residues. Based on results from this study, growers should apply glufosinate or diquat with preharvest glyphosate to maximize crop and weed desiccation, and minimize glyphosate seed residues.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".