Hydrological models for the estimation of ecological flow
Bibliographic record
Abstract
Fixing the ecological flow in the world's rivers contributes to the preservation of river ecosystems and the management of water resources, related to the social and economic aspects; It is the volume of water necessary to maintain a healthy river ecosystem capable of providing goods and services. The hydrological method was the first proposed method to estimate the ecological flow, developed in the 60s and 70s of the 20th century. There are different methods to estimate the ecological flow, such as: hydrological, hydraulic, eco-hydraulic and holistic methods. In the present work, a review of seven hydrological methods was carried out, these are: Asturian, flow permanence curve, Ecuadorian or 5% of the annual average, referential-Peruvian regulations, Scottish or 3 critical months, Swiss and Tennant-Montana (1976). These hydrological methods use historical records of flows from hydrometric stations to quickly and cheaply obtain an easily interpreted result. Tennant-Montana (1976) is the most widely used method in river basins in 25 countries to determine ecological flow, and it is the second most widely used method in the US and Canada. These methods are applicable for an investigation in any river of the Peruvian territory.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".