Comparison of hydrologically based instream flow methods using a resampling technique
Bibliographic record
Abstract
The protection of fish habitat against the impact of water extraction in rivers is a recurring problem in water resources management. As such, a wide range of methodologies is available for the calculation of instream flows. This study focuses on historical streamflow methods that rely solely on hydrometric data for instream flow evaluation. The objectives of the study are to compare different historical streamflow methods and use a jackknife resampling technique to assess the variability of instream flow estimates. Results showed that methods based on a percentage of mean annual flow (MAF) generated higher levels of instream flow protection and showed low spatial and sample size variability. Low spatial variability makes the MAF methods more suitable for calculations of instream flows for ungauged basins. The Q 50 method provided relatively high levels of instream flow protection; however, spatial and sample size variability were higher than those for the MAF methods. Lastly, the results showed that some methods generated low instream flow protection (namely, the Q 90 , 7Q2, and 7Q10 methods), especially for small streams, and thus are not recommended for use.Key words: instream flow, aquatic habitat, water withdrawal, impact assessment.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".