Do intracoelomic telemetry transmitters alter the post‐release behaviour of migratory fish?
Bibliographic record
Abstract
Abstract Electronic tags have become a common tool in fish research, enhancing our understanding of how fish interact with their environment and move among different habitats, for estimating mortality and recording internal physiological states. An often‐untested assumption of electronic tagging studies is that tagged fish are representative of untagged conspecifics and thus show ‘normal’ behaviour (e.g. movement rates, swimming activity, feeding). Here, we use a unique data set for potamadromous walleye (Sander vitreus) in Lake Huron and Lake Erie tributaries to assess whether the lack of appropriate controls in electronic tagging could seriously affect behavioural data. We used fish tagged in previous years and compared their migratory behaviour during the spawning season to fish tagged in a current year at the same location. The objective of the study was to determine whether intracoelomic acoustic tag implantation altered downstream movement of walleye after spawning. Fish tagged in a given season travelled slower downstream from two river spawning sites than fish tagged in previous years. Fish tagged one or two years earlier showed no differences between each other in downstream travel time, in contrast to fish tagged in a given year. Our results support notions that standard collection and intracoelomic tagging procedures can alter short‐term behaviour (i.e. days, weeks, months), and as such, researchers should use caution when interpreting data collected over such time periods. Further, whenever possible, researchers should also explicitly evaluate post‐tagging effects on behaviour as part of their experimental objectives.
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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 | 0.001 |
| 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".