Critical period of weeds interference in pineapple (Ananas comosus [L.] Merr.) crops
Bibliographic record
Abstract
Description of the subject. In crop-weed competition, some periods are critical because they may interfere with the crop production capacity. Therefore, knowing these periods is crucial when deciding on the best time for control and preparation of the Integrated Management Program. Objectives. To determine the Period Prior to Interference, Total Period for Interference Prevention, and the Critical Interference Period in pineapple cultivation. Method. The experimental design consisted of randomized blocks in 2 x 15 factorial arrangement, four replications, and two management systems: weeds control and crop-weeds coexistence; 15 increasing periods of 30 days each after planting: 0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360, 390, 420. In the control treatments, since planting, manual weeding (with hoe) and mechanical weeding (with brush cutter) were done during increasing periods of time, and in the coexistence treatments, the weeds lived together with pineapple plants and, at the end of each period, weeds control was made with mechanical and chemical weeding. Results. The best yields were achieved in the periods of less coexistence with weeds. The critical periods for interference prevention with 5 and 10% of yield losses were 259 and 204 days, respectively. Conclusions. The critical period for interference prevention lasted 14-259 days after planting of the pineapple offsets.
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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".