A Research on the Market Mechanism of Environment Protection in Wuhan City
Bibliographic record
Abstract
Human beings have changed the development and use of water resource from single target to multi-targets, owing to that water resource is an important physical basis for the development of economy and society. This paper explores the market mechanism of water resource and environment protection in Wu Han City in terms of the mechanisms of water right protection, water right market, fees paid by benefactors and protector revenue. Key words: Water resource,Water environment,Market mechanism Resume L’exploitation et l’utilisation des ressources hydrauliques developpent de l’unique objet vers de multiple objets, les ressources hydrauliques sont des conditions materielles importantes du developpement de l’economie nationale et de la societe. Cet article traite les ressources hydrauliques et le mecanisme oriente vers le marche de la protection de l’environnement hydraulique de la ville de Wu han sous quatre angles ci-dessous : le mecanisme de la protection du droit hydraulique, le mecanisme oriente vers le marche du droit hydraulique, le mecanisme du paiement des beneficiaire, le mecanisme du benefice des protecteurs. Mots-cles: Wuhan, les ressources hydrauliques, l’environnement hydraulique, le mecanisme oriente vers le marche 摘 要 人類對水資源的開發利用，已有單一目標向多目標協同發展，水資源是國民經濟和社會發展的重要物質條件。本文擬從水權保護機制、水權市場化機制、受益者付費機制和保護者收益機制四個方面對武漢市水資源及水環境保護市場化機制作些探討。 關鍵詞：武漢；水資源；水環境；市場化機制
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".