WEATHER AT DIFFERENT GROWTH STAGES, MULTIPLE PRACTICES AND RISK EXPOSURES: PANEL DATA EVIDENCE FROM ETHIOPIA
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 study investigates the effects of combinations of climate smart agricultural practices on risk exposure and cost of risk. We do this by examining the different risk components — mean, variance, skewness, and kurtosis — in a multinomial treatment effects framework by controlling weather variables for key stages of crop growth. We found that adoption of combinations of practices is widely viewed as a risk-reducing insurance strategy that can increase farmers’ resilience to production risk. The hypothesis of equality of weather parameters across crop development stages is also rejected. The heterogeneous effects of weather across crop growth stages have important implications for climate change adaptation to maximize quasi-option value. For a country that has the vision to build a climate-resilient economy, this knowledge is valuable to identify a combination of climate smart practices that minimizes production risk under variable weather conditions.
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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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