Control of Jointvetch (Aeschynomene spp.), Establishment and Productivity of Rice as a Function of [Imazapic + Imazapyr] Doses
Bibliographic record
Abstract
We aimed with this work to evaluate the efficiency of the herbicide [imazapic + imazapyr] in the weed control, especially of the jointvetch, and its reflex in rice plant establishment and grain yield. A field study was conducted, where treatments were: control without herbicide application; Kifix® 140g ha-1 (24.5 ga.i. ha-1 imazapic + 73.5 ga.i. ha-1 imazapyr) and Kifix® 280 g ha-1 (49 ga.i. ha-1 imazapic + 147 ga.i. ha-1 imazapyr). We evaluated crop emergence up to 25 days after sowing, crop plant height 12 days after emergence (DAE), jointvetch plant density 31 and 62 DAE, jointvetch dry mass 62 DAE and general weed infestation 62 DAE. At the end of the crop cycle we evaluated grains per panicle, panicle and 1000 grain weight, panicle density and crop grain yield. There is a risk of 15% reduction in the establishment of Clearfield® rice plants when the maximum dose of the herbicide [imazapic + imazapyr] is applied pre-emergence, but under appropriate conditions, the crop development tends to compensate for this reduction in establishment. The herbicide [imazapic + imazapyr] is effective on jointvetch, but under high infestation conditions, as in the present study, a complementary herbicide application is required to achieve adequate control of jointvetch and avoid rice grain yield reduction due to competition.
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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".