Influence factors of ecological environment in Wanjiang River Basin based on RSEI and CASA models
Bibliographic record
Abstract
In this paper, the original RSEI model (including greenness index, humidity index, heat index and dryness index) and their contribution rate (%) are used to evaluate the change of ecological environment. To study how natural and anthropogenic changes in ecological environment affect ecosystem function and how these factors interact, CASA model was used to estimate the correlation between the net primary productivity of vegetation (NPP), population density and land use. This could reveal the influence factors of ecological environment change in the Wanjiang River Basin from 2000 to 2022. The results show that the combing RSEI and CASA models can effectively show the spatial-temporal variation and spatial distribution of NPP of vegetation in Wanjiang Basin. During this period, RSEI of the basin showed an overall upward trend, and the RSEI increased by about 0.1/10 a. The vegetation productivity in most areas was gradually improved, the ecological environment was restored, and the ecological environment quality was gradually improved. The average annual NPP simulated by CASA was 266.81 g C·m -2 ·a -1 . The variation trend of NPP in vegetation showed a slight decrease, but the overall NPP level was basically unchanged. There is a significant correlation between NPP and population density, a negative correlation between NPP and population density areas such as city centers, and a positive correlation between NPP and population density in areas with frequent agricultural activities. From 2010 to 2020, there is a significant positive correlation between population density and land use.
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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".