Passive Integrated Transponder (PIT) Tagging Did Not Negatively Affect the Short‐Term Feeding Behavior or Swimming Performance of Juvenile Rainbow Trout
Bibliographic record
Abstract
Abstract Passive integrated transponder (PIT) tagging is a commonly used procedure to identify fish. However, there is a lack of research on the short‐term effects of such tagging. The purpose of our study was to measure the short‐term effects of PIT tagging on the feeding behavior and swimming performance of juvenile rainbow trout Oncorhynchus mykiss. Three experiments were conducted. The treatment groups in the first two experiments consisted of a control group and a PIT‐tagged group. In the first experiment, we timed the latency to resume feeding before and after the experimental day in fish trained to feed with a light cue. In the second experiment, we recorded the amount of food ingested before and after the experimental day in fish that had been fed to satiation every day for a week prior to the experiment. We found no significant differences between control and PIT‐tagged groups for either the latency to resume feeding (time from providing food to food intake) or the amount of food eaten. The third experiment consisted of a fixed‐velocity swimming performance test in which fish that had been tagged 40 d before the test and whose wounds had healed were compared with fish that were tagged on the day of the experiment. We found no significant differences between the fish that had healed wounds and fish that had just been tagged.
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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".