Assessing and Optimizing Ecological Flow Rates for the Habitat of Zacco platypus in the Tan River
Bibliographic record
Abstract
As rivers face growing environmental challenges due to climate change and the construction of artificial structures, it is essential that we improve river ecosystems to maintain their ecological functions and preserve the health of aquatic habitats. The aim of this study was to assess the aquatic ecosystem health of the lower reaches of the Tan River. We employed the Physical Habitat Simulation System and Hydrologic Engineering Center’s River Analysis System to calculate the ecological flow rate based on the weighted usable area (WUA) of Zacco platypus, which is a representative fish species in the Tan River, and the flow rate relationship curves. By analyzing the flow rates in the Tan River across different seasons from 2012 to 2021, we determined that the seasonal optimal ecological flow rate was 10.21–10.27 m3/s. Meanwhile, the WUAs for spring, summer, and autumn and winter were 90–100%, 95–100%, and 75–100%, respectively. Despite meeting the ecological flow criteria for summer, fall, and winter over 50% of the time, spring fell short at 41%; hence, the Tan River flow rates should be secured particularly in spring. This study highlights the urgency of addressing seasonal variations to ensure the overall health of the Tan River ecosystem.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".