Editorial for the Thematic Series in Agriculture & Food Security: Climate-Smart Agriculture Technologies in West Africa: learning from the ground AR4D experiences
Bibliographic record
Abstract
This Thematic Series on “Climate-Smart Agriculture \nTechnologies in West Africa: learning from the ground \nAR4D experiences” contains seven papers presented by \nresearchers from four West African countries based on \nparticipatory action research conducted since 2012 in \nthe region. These research activities were funded by the \nCGIAR Research Program on Climate Change Agriculture \nand Food Security (CCAFS) through a project titled \n“Developing community-based climate-smart agriculture \nthrough participatory action research in CCAFS benchmark \nsites in West Africa” (see [1]). This research action \nunder the scientific lead of the World Agroforestry Centre \n(ICRAF) aimed to test and validate, in partnership \nwith rural communities and other stakeholders, scalable \nclimate-smart village models for agricultural development \nthat integrate a range of innovative agricultural risk \nmanagement strategies. The project also aimed to enable \nfarmers, developers, managers and policy makers for the \nagriculture sector to develop cost-effective climate-smart \nagriculture (CSA) options that support local sustainable \ndevelopment and enhance livelihood resilience. It is \ntherefore a response to the challenges (degraded lands, \nlow crop productivity, high level of poverty for rural people, \netc.) faced to satisfy the food needs of an increasing \npopulation in the face of a changing climate...
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.049 | 0.021 |
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".