The level of cultivated land security risk in China based on the PNN network
Bibliographic record
Abstract
Currently, cultivated land areas throughout China are differentially susceptible to a variety of natural hazards, social risks, and economic challenges. A risk evaluation model is developed in order to capture the degree of risk to the quality of cultivated land and cultivated land areas in China. Based on the Probabilistic Neural Network (PNN), the model seeks to provide insights/recommendations on the use of cultivated land in China, by employing a set of environmental indicators. Threshold risk levels are established in order to reduce the vulnerability of cultivated lands. Five cultivated land area risk categories are defined. It is shown that more resources should be dedicated to protecting the environment and cultivated land areas in China. By reducing the risk of natural hazards and socio-economic pressures, it is expected that the quality of cultivated land in China can be improved. Finally, regions primarily dedicated to food production should be given additional protection from natural and anthropogenic risks.
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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) | 0.000 | 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 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".