PRE herbicides and POST halosulfuron for purple nutsedge control in tomato grown in plasticulture systems
Bibliographic record
Abstract
Abstract Purple nutsedge is a troublesome weed in tomato grown in plasticulture systems. Field trials were conducted in the fall of 2017 and spring of 2018 at Balm, FL, to evaluate multiple herbicide programs applied pretransplanting (pre-T), post-transplanting (post-T), and pre-T followed by (fb) post-T for purple nutsedge control in plasticulture tomato. Pre-T treatment of sulfentrazone or S-metolachlor alone were ineffective and did not decrease purple nutsedge density compared with the nontreated control. Post-T application of halosulfuron did not reduce purple nutsedge density at 12 wk after initial treatment (WAIT) in fall 2017 but reduced the purple nutsedge density at 17 WAIT in both seasons. Pre-T sulfentrazone or S-metolachlor application fb halosulfuron applied post-T were the most effective treatments and consistently reduced purple nutsedge population in both seasons. Herbicide treatments did not injure or reduce tomato height or yield. Overall, these results suggest sequential herbicide programs, including pre-T application of sulfentrazone or S-metolachlor fb post-T application of halosulfuron generally resulted in greater purple nutsedge control compared with pre-T or post-T application only. Halosulfuron applied post-T is critical to provide season-long purple nutsedge control in plasticulture tomato.
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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) | 0.002 | 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".