A multistate mark–recapture approach to characterize stream fish movement at multiple spatial scales
Bibliographic record
Abstract
We studied movement of a native salmonid, white-spotted char (Salvelinus leucomaenis), in a 1-km tributary in northern Hokkaido, Japan, in May–July 2018. Based on physical mark–recapture of 501 unique individuals and detection by mobile PIT antenna over monthly intervals, a majority of fish (70%–80%) stayed within 60 m of previously released locations, demonstrating what appeared to be restricted movement patterns. However, fixed PIT antenna data showed that as much as 17% of marked individuals emigrated from the study area during the 2-month study period. Probability of emigration did not depend on where in the 1-km segment individuals had been released, indicating that emigration likely represented long-distance movement. Once emigrants made a decision to emigrate, they left the tributary within 1–3 median days by moving downstream in a unidirectional manner, based on detections at a total of three antenna arrays deployed throughout the tributary. Our multiscale analysis provided strong support for co-existence of short- and long-distance movement patterns, and we conclude that movement data at multiple spatial scales complement each other to characterize population-scale movement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".