Bayesian multistage factorial analysis for unveiling multi-indicator effects on synergistic carrying capacity of water resource, environment and ecology: A case study of Ordos
Bibliographic record
Abstract
Water resource, water environment and water ecology are interrelated elements within the watershed system and are of vital importance for the regional sustainable development. Accurately assessing the synergistic carrying capacity of water resource, environment and ecology (abbreviated as WSCC) remains a challenge due to multiple indicators, complicated interactions and multi-dimensional dependencies. This study develops a Bayesian multistage factorial analysis (BMFA) method through integrating Bayesian model averaging (BMA), coupling coordination model (CCM), and multistage factorial analysis (MFA) into a general framework. BMFA can (i) quantify the WSCC within a multi-layer and multi-dimensional evaluation framework as well as solve issue of subjectivity and single-source dependency in weighting, and (ii) reveal the key indicators affecting WSCC as well as reflect their individual and interactive effects. BMFA is applied to Ordos, a typical city facing issues of water shortage, deterioration of water environment and water ecology. The main findings are: (i) the WSCC in Ordos is an overall good status (with the mean value of 0.696) during 2000–2022, evolving from moderate in 2000 to good in 2022; (ii) among all counties, the WSCC value in Hangjin Banner is the highest (0.731) due to abundant per capita water resource, effective pollution control and low reliance on groundwater, and Otuoke Banners has the lowest WSCC value (0.655) because of groundwater overexploitation and scarce natural resources; (iii) the top three indicators affecting the city’s WSCC are urbanization rate (with contribution 58.4%), industrial wastewater treatment operating expenses (26.0%), and wetland coverage (11.1%). The findings reflect the spatial–temporal variation of the city’s WSCC and reveal the main indicators affecting WSCC, which can further provide useful information to synergistically manage water resource, environment and ecology and to support the regional sustainable development.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".