Seasonal activity and depth distribution of resident yellow-phase American eels (<i>Anguilla rostrata</i>) in a large fluvial ecosystem
Bibliographic record
Abstract
Abstract The American eel (Anguilla rostrata) is a catadromous species occupying diverse habitats, but little is known about the specific activities of this elusive nocturnal fish. Mobile radio and acoustic telemetry were used to locate 33 transmitter-implanted resident yellow eels (729 ± 22 mm TL), acquiring 1613 locations from 2014 to 2017 in a 21-km2 slow-flowing section of the upper St. Lawrence River. Measurements of distance moved, water depth, and temperature were used to study activity, homing, and site fidelity. Movement was greatest in spring, late April (422 m, median semi-monthly), after emergence from winter dormancy, from their deepest habitat (3.71 ± 0.06 m) to their shallowest (1.74 ± 0.17 m). Activity was low in early summer and early fall (69 m). As temperature reached a maximum during midsummer (24.3 ± 0.29°C), most eels became more active (106 m) and moved deeper (2.46 ± 0.28 m), probably following prey fish. Activity increased in mid-to-late fall (277 m), decreased considerably at ≲8°C when settling into their winter habitat, and ceased at ≲4°C. Temperatures of ≃10°C (8.1–11.1°C) corresponded with the greatest seasonal activity and spring/fall commercial hoop-net catches. These spatial and temporal habitat insights can help focus sampling and assessment procedures, as well as habitat suitability modelling.
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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 | 0.001 | 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".