Perception of Dew by Cereal Growers in Semi-Arid Climate (Guéné, North Benin)
Bibliographic record
Abstract
West Africa is one of the regions the more impacted by climate change, concerning in particular droughts and lack of fresh water.In this context was carried out a study of the sociological perception of dew as alternate source of water by cereal's growers in Guéné (semi-arid region, north Benin).Ten data collectors were formed to fill out questionnaires, addressed to 100 cultivators in 2014.Data analysis takes into account respondents' gender, their ages and the type of cereal they cultivate.Close to 80% of the growers experienced dew assistance when sowing cereals.During rain shortage, 44% of the farmers rely on dew occurrence to compensate for lack of rain water.Among farmers, 80% account for dew before and during cereals growth.For 99% of growers, dew plays an important role for cereal growth, as stated in scientific literature.However, 17% point out a possible negative role of dew, favoring dissemination of plant diseases.Farmers (87%) are open to any technology capable of collecting enough dew water for agriculture, but they remain skeptical about such discoveries.Responses only slightly vary when considering the gender and the ages of the farmers but they vary strongly when considering the type of cereal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".