Sweet Corn (Zea mays) Cultivar Sensitivity to Mesotrione<sup>*</sup>
Bibliographic record
Abstract
Field studies were conducted in 1999 and 2000 at Simcoe, Ridgetown, and Exeter, Ontario, to evaluate the tolerance of nine sweet corn cultivars to mesotrione, applied preemergence (PRE) at 140 and 280 g ai/ha and postemergence (POST) at 100 and 200 g ai/ha. Urea ammonium nitrate fertilizer (28%) at 2.5% (v/v) and crop oil concentrate at 1% (v/v) were added to POST applications of mesotrione only. All cultivars were tolerant to mesotrione applied PRE. There was no injury, or reductions in plant height or yield with PRE applications of mesotrione at any location in either year. POST applications of mesotrione, particularly at 200 g/ha, caused significant phytotoxicity to ‘Calico Belle’ and ‘Del Monte 2038’. Other cultivars also showed phytotoxic symptoms; however, this injury was much reduced and did not occur at all locations each year. Sweet corn injury by mesotrione increased as rate increased. Del Monte 2038 also had significantly reduced plant height and yields. Other cultivars had no plant height or yield reductions because of POST applications of mesotrione.Nomenclature: Mesotrione; sweet corn, Zea mays L.Additional index words: Crop injury, plant height, triketone.Abbreviations: DAP, days after planting; DAT, days after treatment; HPPD, p-hydroxy-phenylpyruvate dioxygenase; OM, organic matter; PRE, preemergence; POST, postemergence; SU, sulfonylurea.
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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 | 0.000 |
| 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".