Quantifying the combined effects of attempt rate and swimming capacity on passage through velocity barriers
Bibliographic record
Abstract
The ability of fish to migrate past velocity barriers results from both attempt rate and swimming capacity. Here, I formalize this relationship, providing equations for estimating the proportion of a population successfully passing a barrier over a range of distances and times. These equations take into account the cumulative effect of multiple attempts, the time required to stage those attempts, and both the distance traversed on each attempt and its variability. I apply these equations to models of white sucker (Catostomus commersoni) and walleye (Stizostedion vitreum) ascending a 23-m-long flume against flows ranging from 1.5 to 4.5 m·s1. Attempt rate varied between species, attempts, and over time and was influenced by hydraulic variables (velocity of flow and discharge). Distance of ascent was primarily influenced by flow velocity. Although swimming capacity was similar, white sucker had greater attempt rates, and consequently better passage success, than walleye. Over short distances, models for both species predict greater passage success against higher velocities owing to the associated increased attempt rate. These results highlight the importance of attraction to fish passage and the need for further investigation into the hydraulic and other environmental conditions required to simultaneously optimize both attempt rate and passage success.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".