Environmental factors influencing annual sucker (Catostomus sp.) migration into a Great Lakes tributary
Bibliographic record
Abstract
Fish migration in rivers is a growing area of concern as mounting anthropogenic influences, particularly fragmentation from dams and barriers, constitute major threats to global river species diversity. Barriers can impede the movement of fishes between areas critical to the completion of their lifecycle, affecting both population and ecosystem viability. In response, fish passage solutions have been identified as a critical need to maintain fisheries viability in the Laurentian Great Lakes, and around the world. Pivotal to the success of these fish passage solutions is a more complete understanding of the movement phenology and environmental cues that instigate migration. We used a dual-frequency identification sonar (DIDSON) to evaluate environmental triggers of river entry during spring and summer for three size classes of migratory fishes in the Boardman River, a Lake Michigan tributary. Our results indicate that medium size fish (>30 cm and < 50 cm), primarily composed of white sucker Catostomus commersonii and longnose sucker Catostomus catostomus were 21% more likely to enter the river at sunset and 25% less likely at midnight in comparison to midday. Entry rates of medium fish increased 6% for every 1 °C increase in river temperature, 4% for every 1 m3/s increase in river discharge from the day prior, and were reduced by 1% for every 10 cm increase in lake level. Understanding these processes in the tributaries of the Great Lakes is important to inform the fish passage solutions currently being developed for the Boardman River, and to inform management regulations for Great Lakes migratory fishes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".