Risk Assessment and Regionalization of Agro-meteorological Hazards in Jilin Province, China
Bibliographic record
Abstract
Jilin province is one of the major maize-growing regions of China and is also one of the major contributors to the Maize Belt of the world’s Temperate Zone. Agro-meteorological hazards such as drought, waterlogging and cool summer occur with very high frequency and affect grain production and social-economic development in Jilin province. Moreover, both the frequency of these hazards and losses from them are considered to be increasing with global warming. This study presents a methodology for risk analysis and assessment of meteorological hazards to agricultural production in Jilin province, China, based on Geographical Information System (GIS) from the viewpoints of climatology, geography, disaster science, disaster risk analysis, environmental science, and so on. This study can be expected to provide the basis for developing strategies to mitigate agro-meteorological hazards and reducing the losses from them, and adjust the medium and long-term distribution of agricultural activities so as to adapt to environmental changes and to ensure agricultural sustainable development.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".