Climate, crops, and forests: a pan-tropical analysis of household income generation
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
Abstract Rural households in developing countries depend on crops, forest extraction and other income sources for their livelihoods, but these livelihood contributions are sensitive to climate change. Combining socioeconomic data from about 8,000 smallholder households across the tropics with gridded precipitation and temperature data, we find that households have the highest crop income at 21°C temperature and 2,000 mm precipitation. Forest incomes increase on both sides of this agricultural maximum. We further find indications that crop income declines in response to weather shocks while forest income increases, suggesting that households may cope by reallocating inputs from agriculture to forests. Forest production may thus be less sensitive than crop production to climatic fluctuations, gaining comparative advantage in extreme climates and under weather anomalies. This suggests that well-managed forests might help poor rural households to cope with and adapt to future climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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