Quantifying the effects of pop-up satellite archival tags on the swimming performance and behavior of young-adult mahi-mahi (<i>Coryphaena hippurus</i>)
Bibliographic record
Abstract
Pop-up satellite archival tags (PSATs) have been used to demonstrate habitat utilization and large-scale migrations of aquatic species and are a critical tool to manage highly migratory fish populations. Use of PSATs has increased in recent years; however, few studies have investigated the physiological and behavioral effects of carrying a PSAT. To address this gap, young-adult mahi-mahi (Coryphaena hippurus; 25–35 cm fork length) were tagged with miniature PSATs and assessed in a two-part experiment utilizing swim tunnel respirometry and behavioral analysis of free-swimming individuals. Swim tunnel respirometry revealed significant reductions in the critical and optimal swimming speeds of tagged fish (10.2% and 20.9%, respectively), as well as significant reductions in maximum metabolic rate and aerobic scope (16.1% and 21.4%, respectively). In contrast, mean and maximum velocity, acceleration, total distance traveled, survival, and feeding success of free-swimming tagged fish showed no impacts of tagging compared with untagged conspecifics held in the same tank. The results of this study highlight the importance of considering multiple methodologies to assess the impacts of tagging fish and provide insight into the data collected by PSATs deployed on wild fish.
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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 | 0.001 |
| 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".