Ecological Regulation of Hydraulic Engineering Projects in USA and Canada and Its Reference for China
Bibliographic record
Abstract
Systematic analysis was done of the management frameworks and models of ecological regulation of hydraulic engineering projects in USA and Canada.In terms of indirect(macro) management,the USA and Canada exercise ecological regulation through management of water licensing,establishment of reserved water rights,setting-up of water quality standards,protection of endangered species and prescription of environmental flow.However,their direct management includes mainly,ESA(Endangered Species Act) compliance review of federal hydraulic engineering projects,and licensing management of non-federal hydraulic power stations(above 5 MW) by the Federal Energy Regulatory Commission(FERC).Analysis of the problems existing in ecological regulation and management of hydraulic projects in China reveals that China has not yet had any effectively established ecological regulation management system and mechanism effectively established.It is,hence,suggested that ecological regulation and management in China be intensified through specifying ecological demands,determining strategies for implementing ecological regulation;strengthening basic research in this aspect and consummating the management system.With a watershed ecological regulation and management system being gradually set up,operating mechanism for ecological regulation of hydraulic projects could be established and created through establishment of the mechanisms for ecological compensation,participatory negotiation,information sharing,and monitoring and feedback etc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".