Influence of Harvest Aid Herbicides on Seed Germination, Seedling Vigor and Milling Quality Traits of Red Lentil (Lens culinaris L.)
Bibliographic record
Abstract
Most red lentil produced worldwide is consumed in dehulled form, and post-harvest milling and splitting qualities are major concerns in the secondary processing industry. Lentil producers in northern temperate regions usually apply pre-harvest desiccants as harvest aids to accelerate the lentil crop drying process and facilitate harvesting operations. This paper reports on field studies conducted at Scott and Saskatoon, Saskatchewan, Canada in the 2012 and 2013 cropping seasons to evaluate whether herbicides applied as harvest aids alone or tank mixed with glyphosate affect seed germination, seedling vigor, milling, and splitting qualities. The site-year by desiccant treatment interaction for seed germination, vigor, and milling recovery yields was significant. Glyphosate applied alone or as tank mix with other herbicides (except diquat) reduced seed germination and seedling vigor at Saskatoon and Scott in 2012 only. Pyraflufen-ethyl (20 g ai ha-1) applied with glyphosate as well as saflufenacil (36 g ai ha-1) decreased dehulling efficiency, while saflufenacil and/or glufosinate with glyphosate reduced milling recovery and football recovery, although these effects were inconsistent. Application of diquat alone or in combination with glyphosate exhibited more consistent dehulling efficiency gains and increases in milling recovery yield. Significant but negative associations were observed between glyphosate residue in seeds and seed germination (r=-0.84, p<0.001), seed vigor (r=-0.62, p<0.001), dehulling efficiency (r=-0.55, p<0.001), and milling recovery (r=-0.62, p<0.001). These results indicate application of diquat alone or in combination with glyphosate may be a preferred option for lentil growers to improve milling recovery yield
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".