EFFICACY OF DIFFERENT ESTABLISHMENT METHODS AND WEED MANAGEMENT PRACTICES ON WEED DENSITY, WEED DRY MATTER, WEED CONTROL EFFICIENCY AND YIELD UNDER RAINFED LOWLAND RICE
Bibliographic record
Abstract
A field experiment was conducted during the kharif season of 2011-12 at Agronomy Research Farm, Central Research Station, Orissa University of Agriculture and Technology, Bhubaneswar. The experiment was laid out in split-plot design to find out the effect of various establishment methods and weed management practices on different weed parameters such as weed density (Grasses, Sedges and Broadleaf weeds), weed dry matter, weed control efficiency and grain yield under rainfed lowland rice. Experiment resulted that Weed parameters like total weed density (8.0 no. m-2), weed dry matter (6.4g.m-2) and weed index were lowest in system of rice intensification (SRI) at 30 days after transplanting/sowing (DAT/S). With respect to weed management practices total weed density (7.53 no.m-2),weed dry matter (2.3 g m-2) was recorded lowest in pyrazosulfuron-ethyl @20 g.ha- and highest weed control efficiency 97.04 percent were recorded in conoweeder. Grain yield of 5.02 t ha-1 and 4.76 t ha-1 were recorded in SRI and conoweeder respectively. While highest straw yields were recorded in SRI (5.8 t ha-1) and conoweder (5.5 t ha-1). Severe infestation of weeds reduced the yield by 32, 38, 39 and 52 percent in transplanted, SRI, drum seeded and direct seeded rice
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".