Crop Response to Carryover of Mesotrione Residues in the Field
Bibliographic record
Abstract
Two field residue studies were conducted from 2005 to 2007 in Simcoe, Ontario, Canada, to evaluate the effects of mesotrione soil residues on injury, plant dry weight, and yield of sugar beet, cucumber, pea, green bean, and soybean and to verify the potential of reducing a 2-yr field-residue study (conventional residue carryover) to a 1-yr field study (simulated residue-carryover study) by growing these crops in soil treated with reduced rates of mesotrione applied in the same year. There was a significant difference in mesotrione carryover between 2006 and 2007 and differences between years can be explained by differences in soil pH and soil moisture. The conventional and the simulated residue-carryover studies successfully measured mesotrione persistence and rotational crop sensitivity. Both studies showed that sugar beet was the most-sensitive crop with injury, plant dry weight reduction, and yield loss because of mesotrione residues as high as 100%. Green bean was the next most-sensitive crop to mesotrione residues followed by pea, cucumber, and soybean. The simulated residue-carryover study provided a more-rigorous test of rotational crop sensitivity to mesotrione residues than the conventional residue-carryover study, especially at higher rates for the more-sensitive crops. For the other crops, responses to mesotrione residues were similar between the conventional and simulated residue-carryover studies.
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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 | 0.001 | 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) | 0.001 | 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".