Farmers’ Willingness as Well as Its Compensation Strategy to Prevent and Control Agricultural Non-Point Source Pollution in Water Source Areas-Based on a Comparative Analysis of Pesticide and Fertilizer Inputs
Bibliographic record
Abstract
This paper took the voluntary pesticide and fertilizer reduction ratios of the farmers as the substitute variables for farmers' willingness to prevent and control the agricultural non-point source pollution. Then, the paper adopted an improved double-bounded dichotomous choice (DBDC) to obtain farmers’ voluntary pesticide and fertilizer reduction ratios and the corresponding compensation attitudes of the farmers. The research found that, in contrast to fertilizers, pesticides are a stronger output-related factor for farmers, and any reduction in pesticides would cause great output fluctuations. After the influencing factors of the farmers’ compensation attitude towards pesticide and fertilizer reduction were subject to ordinal regression analysis, the main influencing factors of farmers’ compensation attitude towards fertilizer and pesticide reduction were obtained respectively, which had further proved that for farmers, compared with pesticides, the risk of fertilizer reduction is lower, and their attitudes are quite different, but as long as the compensation is sufficient, they are willing to take more stringent measures.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".