Forest Biological Disaster Control Behaviors of Forest Farmers and Their Spatial Heterogeneity in China
Bibliographic record
Abstract
With more and more loss caused by forest biological disasters (FBDs) in China, forest farmers, as one of the most important stakeholders, are participating in the control. In this study, the ordinary least squares model, simultaneous equation model, and propensity score matching method were used with the data from 818 surveys conducted in the typical FBD outbreak provinces, to reveal the FBD control behaviors of forest farmers and their differences between western and eastern regions of China. The results indicated the following. (1) Household factors: forest farmers could timely take control measures. An increase of 1 ha in the area of occurrence would increase the control measures by 3.26 ha. However, the control measures can only reduce 50% of the economic loss caused by FBDs and cannot effectively control the spread trend of FBDs. There are issues, including an insufficient and old labor force, insufficient technology support, and low consciousness of ecology protection. (2) External factors: forest farmers would increase control when the temperature rises, and reduce control when rainfall increases. After village committees unify organizing the control, their participation enthusiasm would increase, which would have a substitution relationship with the household investment. (3) Regional difference: the eastern region of China has higher figures than the western in terms of outbreak area, economic losses, control measures, and pesticide cost. If the western forest farmers have the control funds as the eastern forest farmers have, and the eastern forest farmers have the control intensity as the western forest farmers have, the overall FBD control effect would be better.
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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 | 0.000 | 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".