Weather effects on maize yields in northern China
Bibliographic record
Abstract
SUMMARY In the present study, the effect of weather on maize yields in northern China was examined using data from 10 districts in Inner Mongolia and two in Shaanxi province. A regression model with a flexible functional form was specified on the basis of agronomic considerations. Explanatory variables included in the model were seasonal growing degree days, precipitation, technological change (e.g. adoption of new crop varieties, improved equipment, better management, etc.) and dummy variables to account for regional fixed effects. Results indicated that a fractional polynomial model in growing degree days could explain variability in maize yields better than a linear or quadratic model. Growing degree days, precipitation in July, August and September, and technological changes were important determinants of maize yields. The results could be used to predict potential maize yields under future climate change scenarios, to construct financial weather products and for policy makers to incentivize technological changes and construction of infrastructure (e.g. irrigation works) that facilitate adaptation to climate change in the agricultural sector.
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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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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".