The Trend Analysis on China's Agricultural Natural Risks and Improvement of the Ability of Disaster Mitigation
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
In accordance with the concept of the agricultural natural disasters formed in China, by means of over 20 years of major disasters occurred panel data of recorded, it defined and measured the rates of disaster reduction and disaster affected, and gives the interpretation of mitigation agricultural natural disasters. According to the extent and the area of distribution of a variety of disasters in the losses of crop, use of basic statistical methods to analyze the development trend and the affected area's variation of various disasters. Such as, it discussed the natural disaster's long-term changes in the diversity and complexity of features. Finally, from the perspective of macro policy it studies the responds and mitigation measures to cope with agricultural natural disasters.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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