Do natural history data predict the movement ecology of fishes in Lake Ontario streams?
Bibliographic record
Abstract
Little is known about the movements of most stream fishes, so fisheries managers often rely on natural history data from the literature to make management decisions. Observations of over 15 000 individuals from 37 species across 3 years were used to evaluate four aspects of the reliability of literature data for predicting the movement behaviour of stream fishes: (i) water temperature when fish enter streams; (ii) reasons for moving into the streams; (iii) stream residence times of migrants; and (iv) relative use of lake and stream habitats. Comparisons of our data for arrival times in the streams, water temperature at arrival, and time spent in the streams were highly correlated with literature data, whereas relative use of the lake was not. Further, our detailed data revealed two novel findings: (1) in many species juveniles were also moving into streams, even in those species where adults were clearly spawning in the streams; and (2) adult-sized individuals were moving into streams for nonreproductive purposes. Our results suggest that fishery managers can confidently use natural history information to gain general insights into the movement ecology of fishes, but should also recognize that this information remains incomplete in important ways.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".