Movement of sonically tagged bluespine unicornfish, <i>Naso unicornis</i>, in relation to marine reserve boundaries in Rodrigues, western Indian Ocean
Bibliographic record
Abstract
Abstract The lagoon fishery of Rodrigues has considerable socio‐economic importance; however, catches have declined by 50% in recent years. The bluespine unicornfish, Naso unicornis (Forsskål 1775) is an important component of the fishery. To begin to assess whether marine reserves might benefit this species, sonic telemetry was used to track individual fish in one of Rodrigues' four marine reserves. Seven unicornfish were caught in the Grand Bassin reserve and tagged with abdominally implanted acoustic tags. Over a period of 57 days their locations were determined up to 21 times using a hand held hydrophone and receiver. Individual minimum convex polygon (MCP) home ranges varied from ∼10 000 m2 to ∼274 000 m2 and were not correlated with fish size. Kernel estimates of core areas (50% utilization distribution) varied from ∼5000 m2 to ∼175 000 m2. All seven fish stayed within the marine reserve and the largest home range occupied less than ∼2% of the area of the marine reserve. Fish remained on the outside edge of the lagoon on the shallow reef slope and among coral patches. These findings suggest that bluespined unicornfish biomass and numbers should increase if the Grand Bassin marine reserve is closed to fishing. Long‐term monitoring will, however, be required to demonstrate this outcome. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 | 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".