Upstream and Downstream Dispersal Behavior of Hard- and Soft-Released Juvenile Atlantic Salmon
Bibliographic record
Abstract
Abstract Failure of reintroduction efforts of extirpated populations is thought to be linked to maladaptive behaviors exhibited by captive-bred individuals in the environment where they are released. Soft-release conditioning tactics attempt to reduce maladaptive behaviors by providing reintroduced animals an acclimatization period prior to release. We used implanted passive integrated transponder tags and antennae to monitor the spatial and temporal dispersal behavior of captive-bred Atlantic Salmon Salmo salar that were acclimatized for 6 d prior to release (soft-release), with fish that were directly released (hard-release) into East Duffins Creek in Ajax, Ontario, Canada. In total, 232 of the 610 tagged fish (38%) dispersed from the release site. Downstream spatial dispersal did not differ significantly between the hard-release (32%, n = 98 of 310) and soft-release fish (30%, n = 91 of 300), but the hard-release fish were significantly more likely to move upstream (11%) than were the soft-release fish (3%). Timing of dispersal also significantly differed between the two groups: soft-release fish were detected dispersing, on average, approximately 15 d earlier than hard-release fish. These results suggest that soft-release tactics do affect dispersal behavior, and the findings will be of particular interest to fisheries management agencies that are charged with improving the success for stocking salmonids as part of reintroduction efforts.
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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".