Preharvest Applications of Glyphosate Affect Emergence and Seedling Growth of Field Pea (Pisum sativum)<sup>1</sup>
Bibliographic record
Abstract
Field experiments were conducted in 1994 and 1995 at Vegreville, Legal, and Lacombe, AB, to determine the effects of a preharvest application of glyphosate on seedling emergence and growth of field pea. Glyphosate was applied at 0.9 kg ai/ha at each of the three crop development stages, as determined by seed moisture content (SMC), to determinate (‘Ascona’ and ‘Radley’) and indeterminate (‘Miko’ and ‘Trapper’) cultivars. Applying glyphosate when the SMC was less than 30% had little to no effect on seedling emergence but reduced seedling shoot fresh weight in two of six experiments. Applying glyphosate at SMC above 40% reduced seedling emergence and shoot fresh weight in two and three of the six experiments, respectively. Reductions in seedling emergence and shoot fresh weight were greater from seeds collected from the top than from seeds collected from the bottom one-third of sprayed plants. Differences in response between determinate and indeterminate cultivars occurred, but there was no consistent trend. Given the variable maturity in most fields and on individual pea plants, applications of preharvest glyphosate to peas destined for seed production may decrease seed germination and biomass accumulation.Nomenclature: Glyphosate; field pea, Pisum sativum L. ‘Ascona’, ‘Miko’, ‘Radley’, ‘Trapper’.Additional index words: Crop desiccation, determinate and indeterminate pea cultivars, harvest aid.Abbreviations: SMC, seed moisture content.
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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 | 0.000 | 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 | 0.001 |
| 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".