Evaluation of water resources carrying capacity in the Yellow River Basin: a Hu Huanyong Line perspective
Bibliographic record
Abstract
ABSTRACT Water resources carrying capacity (WRCC) is vital in safeguarding regional ecological balance and avoiding over-exploitation of water resources. This study constructed a multi-dimensional evaluation index system integrating water resources, social economy, residents’ life, and ecological environment and applied the Technique for Order Preference by Similarity to an Ideal Solution model to evaluate the WRCC of the Yellow River Basin from 2011 to 2020. The spatial and temporal characteristics of WRCC and the main obstacle factors are analyzed according to the Hu Huanyong Line. The findings showed that the WRCC comprehensive index (Ci) exhibited marginal improvement (11.25% increase) but remained critically overloaded, with values fluctuating between 0.076 and 0.092. Spatial analysis demonstrated a distinct west–east gradient, with Ci values decreasing from 0.096 (west of the Hu Line) to 0.068 (east). This decrease correlates inversely with the intensity of regional development. Systemic diagnostics identified water resources (49.03) and ecological factors (43.03) as dominant constraints, with per capita water availability (43.75) and ecological water utilization rate (40.54) jointly accounting for 84.29 obstacles. Spatial heterogeneity manifested through divergent constraint patterns: water scarcity intensified eastward, while ecological water deficits worsened westward. The results can provide support for water resources management and utilization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".