Bibliographic record
Abstract
Monitoring and reducing Water pollution is critical for the sustainable and green development of socio-economy. This paper combined the concept of Grey Water Footprint and Decoupling theory to evaluate the water pollution status in Yangtze River and the related Economic Belt from 2003 to 2018. By understanding the pollution status, a clearer insight into whether Yangtze River achieved decoupling and where to further improve water quality could be found. According to the result, although some regions, such as Yunnan and Jiangxi, did not display a descending pattern during the period, Yangtze River Economic Belt as a whole showcased a steady decline in total GWF since 2010. In regarding to the economic development, every region within Yangtze River Economic Belt significantly increased their GDP throughout the period. Therefore, according to Tapio’s decoupling model, it was always decoupling status for Yangtze River Economy from 2003 to 2018, and it has been strong decoupling status for 7 years. This paper provides some information and references for the formulation of water resources management policies, and finally promotes the green development of the areas in the Yangtze River Economic Belt.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".