Evaluation of external operculum loop tags to individually identify cage‐cultured Atlantic halibut <i>Hippoglossus hippoglossus</i> in commercial research trials
Bibliographic record
Abstract
The growth, survival and tag retention of double-tagged [external FT4 lock-on (FT4) and internal passive integrated transponder (PIT)-tagged] Atlantic halibut Hippoglossus hippoglossus were compared to internal PIT-tagged controls in a randomized trial. The objective was to assess the suitability of these tags for monitoring the performance of individual fish in longitudinal trials under commercial cage-culture conditions in the lower Bay of Fundy, New Brunswick, Canada. The FT4 tags were chosen due to their similarity to tags used by investigators to track H. hippoglossus in the wild. A subset of the population randomly received an external FT4 tag inserted through the operculum and were monitored over a 1105 day period. The specific growth rate of FT4-tagged fish was significantly reduced in the first sea summer with no significant difference observed for the remainder of the trial. The differential growth in the first sea summer created a relative size advantage, permitting controls to increase in size significantly faster than FT4 fish in all subsequent periods. The FT4 tags did not significantly influence survival under normal commercial cage-culture conditions. Results, however, suggest that the survival of FT4-tagged H. hippoglossus may be compromised during stressful handling events. Tag retention of FT4 tags was acceptable with 76% of tags remaining at the end of the 1105 day trial. FT4 tags proved to be an effective method to identify individual H. hippoglossus, with the caveat that they seriously bias productivity measures in commercial research trials.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".