Visible Implant Elastomer Color Determination, Tag Visibility, and Tag Loss: Potential Sources of Error for Mark–Recapture Studies
Bibliographic record
Abstract
Abstract Errors in visible implant elastomer (VIE) color determination may exert stronger influences on mark–recapture data quality than poor tag visibility and tag loss. I applied individual VIE tags to 567 wild long-snouted seahorses Hippocampus guttulatus using four fluorescent colors (red, orange, green, and yellow). Given VIE tag data were compared with tag data recorded by observers as they released recently tagged individuals back to initial capture locations. During releases, 13.3% of VIE tags were incorrectly read, primarily because of confusions between orange and red markings and between green and yellow markings. Tags were partially invisible in 5% of released individuals; yellow and green markings were the least visible. Whole or partial tag loss was 2.3% within 14 months of tagging. The ability to correctly determine VIE tag colors or detect markings varied among observers and according to the VIE tag color employed, skin color, and shade of the skin color (e.g., light versus dark green). Observer experience did not influence ability to correctly determine VIE colors or detect tags. Pilot studies should precede mark–recapture studies employing multiple VIE colors to identify strategies for reducing confusion among colors in addition to evaluating tag visibility, tag loss, and tag effects on life history rates.
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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 | 0.045 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".