The Need for Reporting Rationale and Detailed Methods in Studies that Surgically Implant Fish with Electronic Tracking Devices
Bibliographic record
Abstract
Abstract Each year, thousands of fishes are tagged with electronic devices to study their biology and inform fisheries management. Such research assumes that the process of capturing, tagging, and then holding fish to allow them to recover before release (i.e., the “tagging process”) does not alter the physiology, behavior, and survival of these fish. However, the fish can experience physiological challenges during the tagging process that may affect their behavior and survival. We have observed that the rationale used to establish protocols for holding durations and conditions of fish before and following surgery has received little attention. Here, we provide a perspective that: (1) provides an overview of the tagging process and its effects on the physiology, behavior, and survival of fish; (2) highlights the diverse holding conditions and durations used by researchers (that are often inadequately described and seem arbitrary); and (3) identifies key research needs. We conclude that decisions of whether, how, and for how long to hold tagged fish before release depend on diverse circumstances that need to be evaluated by researchers. We recommend that researchers explicitly report the details of how, when, where, and why tagged fish are held to facilitate protocols that benefit fish welfare, science, and management.
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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 | 0.781 | 0.873 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".