Environmental Consequences of the Sanmenxia Hydropower Station Operation in Lower Yellow River, China
Bibliographic record
Abstract
The ecological and environmental impacts of the Sanmenxia Hydropower Station on the lower reach of the Yellow River were examined based on the monitoring data, since the 1970s, of the Sanmenxia Hydropower Station’s normal operation. Different operation modes of the reservoir were considered in addition to the effects of tributaries downstream of Sanmenxia. The environmental consequences were evaluated in terms of the total ion content (TIC), since it is regarded as the most important index reflecting the status of natural water quality in the Yellow River. However, the ecological consequence was assessed according to the severity level of the no-flow events, which has become the major concern of the Yellow River Authorities in recent years. The TIC monitored at the hydrological stations along the lower Yellow River was closely related to the reservoir’s operation mode and the inflow from the tributaries between Sanmenxia and Huayuankou. Also, the variation of the severity level of the no-flow events is primarily controlled by the operation modes. The present study is of fundamental importance to determine the relative optimal operation mode of the hydropower station, so as to harmonize various purposes and constraints such as a decrease of the TIC and the severity level of the no-flow events, an increase in power generation, irrigation, and flood prevention.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".