Meeting the Local Rice Needs in Nigeria through Effective Weed Management
Bibliographic record
Abstract
Nigeria is blessed with natural and human resources required to produce rice to meet its local consumption. These resources are spread across all the agro-ecological zones of the country. Despite the availability of these resources, rice production in Nigeria could still not meet the local consumption and over 40% of the crop is imported annually to complement the local production. Several studies have revealed biotic and abiotic stresses as the major impediment to the production of the crop. Weeds are the most prominent of all the stresses across all the ecologies in term of yield reduction, labour demand and control cost. Yield losses of 35-75% and 70-100% have been recorded in low land and upland ecologies respectively as a result of weed interference. The problem of weed is worse than any of the other stress mentioned above except for water stress in low land ecology. This paper reviews the various weed management options that could be adopted for optimal rice yield, by the resource poor farmers of Nigeria.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".