Dealing with weedy problems in agriculture: the role of three agricultural land use management practices in the forest‐savanna ecological zone of Ghana
Bibliographic record
Abstract
An important limiting factor on labour‐intensive small‐scale agricultural production in Ghana is competition from weeds for environmental resources, such as soil nutrients, moisture and sunlight. This article combines primary social research based on surveys and in‐depth interviews, and ecological research based on experiment and secondary research to explore the efficacy of three land use management practices, compared with their alternatives, in dealing with on‐farm weed problems in Gyamfiase‐Adenya‐Obom, Ghana. The fallow management practice of >3 years of fallow showed significantly greater promise of suppressing weeds than ≤3 years of fallow. Mulching slashed vegetation, as a land preparation practice, was also consistently better at reducing weed densities than burning the slashed vegetation. The study indicated that while more frequent weeding was generally more effective in suppressing weed densities than less frequent weeding, the effect of weeding in significantly reducing weed densities was not associated with weeding frequency per se but with how carefully weeding was accomplished.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".