Bioenergetic Habitat Suitability Curves for Instream Flow Modeling: Introducing User-Friendly Software and its Potential Applications
Bibliographic record
Abstract
Abstract Habitat suitability curves (HSCs) are the biological component of habitat simulation tools used to evaluate instream flow management trade-offs (e.g., the physical habitat simulation model). However, traditional HSCs based on empirical observations of habitat use relative to availability have been criticized for generating biased estimates of flow requirements and for being poorly transferable across locations. For fish like salmonids that feed on drifting invertebrates, bioenergetics-based foraging models that relate habitat conditions to net energy gain offer an alternative approach that addresses some of these shortcomings. To make this technique more accessible for practitioners, we present free and user-friendly software for generating bioenergetics-based HSCs. The software also allows sensitivity analyses of HSCs to factors like fish size or prey abundance as well as direct integration of hydraulic data. While some caveats remain, bioenergetic HSCs should offer a more rigorous and credible means for quantifying habitat suitability for instream flow modeling.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".