Cultivated Land Area Change in Shenzhen and Its Socio-Economic Driving Forces Based on STIRPAT Model
Why this work is in the frame
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Bibliographic record
Abstract
The purpose of this paper is to analyze the relationship between prosperous level and cultivated land area were analyzed in Shenzhen city, Method of the STIRPAT model. The results showed that there was no main cause for cultivated land reduction in Guangzhou city (Zhang, 1999). However, population change, changes of the urbanization rate and proportion of the tertiary-industry added value to regional GDP of the area all play important role in the cultivated land reduction. In the scope of observational data, the relationship between the prosperous level and the cultivated land area was not similar to the environmental kuznets curve (EKC). Accordingly, several suggestions were proposed in the study to mitigate the pressure of cultivated land reduction, including population control, urbanization level improvement, industrial structure adjustment, and economic growth mode transition, etc (Cai & Zhang, 2005).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it