Relationship between Climate-Shaped Urbanization and Forest Ecological Function: A Case Study of the Yellow River Basin, China
Bibliographic record
Abstract
Amidst the compounded challenges posed by global climate change and urbanization on forest ecosystems, the integration of urbanization control measures within a climate-focused framework may offer an avenue for breakthroughs. This study delves into the impact of climate, specifically hydrothermal conditions, on the complex interplay between urbanization (Urb) and forest ecological function (Eco) in the Yellow River Basin (YRB) in China. Our findings reveal: (1) The application of a coupled coordination model reveals a stronger alignment between urbanization and forest ecological function in the warm and humid regions in the YRB. (2) Through the cross-sectional threshold regression model, we elucidate the diverse responses of Urb to Eco across varying climate gradients. Among them, annual precipitation shows a double-threshold effect at 532.34 mm and 694.18 mm. As precipitation increases, the impact of Urb shifts from negative to positive on Eco. Moreover, in regions with precipitation below 532.34 mm and above 694.18 mm, the absolute value of response coefficients of Eco to Urb is amplified. Annual average temperature displays a single-threshold effect at 10.11 °C, leading to a transition from negative to positive impact as temperature rises. This study establishes the climate-based threshold system that governs the urbanization–forest ecological function relationship.
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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".