Sustainable Small-Scale Agriculture in Semi-Arid Environments
Bibliographic record
Abstract
For at least the past 8000 years, small-scale farmers in semi-arid environments have had to mitigate shortfalls in crop production due to variation in precipitation and stream flow.To reduce their vulnerability to a shortfall in their food supply, small-scale farmers developed short-term strategies, including storage and community-scale sharing, to mitigate inter-annual variation in crop production, and long-term strategies, such as migration, to mitigate the effects of sustained droughts.We use the archaeological and paleoclimatic records from A.D. 900-1600 in two regions of the American Southwest to explore the nature of variation in the availability of water for crops, and the strategies that enhanced the resilience of prehistoric agricultural production to climatic variation.Drawing on information concerning contemporary small-scale farming in semi-arid environments, we then suggest that the risk coping and mitigation strategies that have endured for millennia are relevant to enhancing the resilience of contemporary farmers' livelihoods to environmental and economic perturbations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".