The Application of GIS as an Assessment and Planning Tool for Smallholder Irrigation Market Development: a case study from the West African Sahel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article outlines a regional-scale assessment methodology using geographic information systems (GIS) as a source of value-added information to identify and prioritize areas within Africa where an irrigation and market-based poverty alleviation model could potentially be applied to benefit poor, small-scale farmers. This assessment methodology, Poverty Reduction through Irrigation and Smallholder Markets (PRISM), is being piloted by a US-based private voluntary organization to assist development organizations to better target and identify smallholder communities for pro-poor market-led interventions that will ultimately boost farm income and move large numbers of the rural poor out of poverty. The Sahel region of West Africa is presented as a case study for the piloting of a GIS scoping methodology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it