Swimming performance of upstream migrant fishes in open-channel flow: a new approach to predicting passage through velocity barriers
Bibliographic record
Abstract
The ability to traverse barriers of high-velocity flow limits the distributions of many diadromous and other migratory fish species, yet very few data exist that quantify this ability. We provide a detailed analysis of sprint swimming ability of six migratory fish species (American shad (Alosa sapidissima), alewife (Alosa pseudoharengus), blueback herring (Alosa aestivalis), striped bass (Morone saxatilis), walleye (Stizostedion vitreum), and white sucker (Catostomus commersoni)) against controlled water velocities of 1.54.5 m·s 1 in a large, open-channel flume. Performance was strictly voluntary: no coercive incentives were used to motivate fish to sprint. We used these data to generate models of maximum distance traversed, taking into account effects of flow velocity, body length, and temperature. Although the maximum distance traversed decreased with increasing velocity, the magnitude of this effect varied among species. Other covariate effects were likewise variable, with divergent effects of temperature and nonuniform length effects. These effects do not account for all of the variability in performance, however, and behavioral traits may account for observed interspecific differences. We propose the models be used to develop criteria for fish passage structures, culverts, and breached dams.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".