Advances in tagging syngnathids, with the effects of dummy tags on behaviour of <i>Hippocampus guttulatus</i>
Bibliographic record
Abstract
Artificial marking and tagging techniques have been used to study movement, population dynamics, behaviour, ecology, survival and growth of at least 25 syngnathid species. External necklace-style tags and injection of visible implant elastomer have been the most used techniques, uniquely identifying hundreds of individual syngnathids to study population dynamics, mortality, behaviour, ecology and growth in at least 13 and 12 species, respectively. Only two studies, both on larger syngnathid species, have tested the use of internal or electronic tags. This new case study reveals that dummy tags, weighing up to 6% of individual body mass, have minimal effect on normal ex situ behaviour of the long-snouted seahorse Hippocampus guttulatus, a smaller syngnathid. In paired aquarium trials, tags did not affect movement, holdfast use or general behavioural state, and only had a short-term effect (1 day) on vertical orientation. Tagged H. guttulatus gained more mass during the 5 day trials, a result which warrants further exploration but indicates that tags did not reduce feeding. This study shows promise for using electronic tagging to study H. guttulatus and similarly sized syngnathids in the wild.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".