Bibliographic record
Abstract
In spite of the great economic potential of cowpea as both domestic and commercial crop, a number of constraints, which include insect pests, diseases and weeds, limits its production in West and many parts of Africa. Weeds reduced cowpea yield and value by competing for light, water and nutrients. Cowpea suffers from weeds particularly when the crop is in the early growth stages before ground cover. Yield losses cause by weeds alone in cowpea production can be as high as 76% depending on the cowpea cultivar, environment and weed management practices. A timely weed removal at the critical period, which falls within the first 40 days of cowpea growth, would help to prevent an unacceptable yield. Weed management in cowpea has been with low technology. Hand weeding is the most widely used weed control method in cowpea but they are usually expensive and labour intensive. Cultural practices such as narrow row spacing and planting of early maturing varieties are also used for weed control in cowpea. Herbicides, which are relatively easy to use and less expensive, have not been widely adopted for weed control in cowpea. There are limited number of selective herbicides with wide spectrum for weed control in cowpea. However, an integrated practices that involved pre-emergence weed control using herbicides or physical weeding, and a supplementary weed removal that would ensure weed control up to 40 days after cowpea emergence could substantially prevent yield losses associated with weed interference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".