Distinct diel and seasonal behaviours in rainbow trout detected by fine-scale acoustic telemetry in a lake environment
Bibliographic record
Abstract
The fine-scale behavioural activities of rainbow trout (Oncorhynchus mykiss) in nature are not well understood, but are of importance for identifying interactions with the ecosystem and of interest to conservationists and recreational anglers. We have undertaken a high-resolution acoustic telemetry study to identify the distinct movement patterns of 30 rainbow trout in a freshwater lake, specifically examining swim speed, area of movement, and site preference in both summer and winter. Activity levels were reduced in winter compared with summer across all fish, but ranking of individuals was consistent. In summer, 16/30 fish displayed diel movement, in which they travelled to a different area of the lake at dawn and returned at dusk, while other fish maintained their site preference regardless of the time of day or swam more randomly throughout the lake. These patterns were minimized in winter, where there was a reduction in cross-lake movement under ice and only 4/30 fish displayed diel movement. Winter conditions may limit the capability (physiological limitations) and (or) motivation (prey availability) for diel behaviours observed in summer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".