Accounting for detection gaps when evaluating reef fish habitat use in an acoustic array
Bibliographic record
Abstract
Understanding the relationship between habitats and the distribution of fishes is critical to effective survey design and spatial management. Determining reef fish habitat utilization patterns from passive acoustic arrays is challenging for the following reasons: (i) habitat classifications must be meaningful to the species, (ii) the array must contain the species’ home range, and (iii) the probability of detection may differ among habitats within the array. We conducted a multi-year tracking study in the marine protected areas (MPAs) of Dry Tortugas, Florida, using a calibrated passive acoustic array deployed over habitats classified by type (reef, rubble, sand), rugosity (high, medium, low relief), and patchiness (contiguous, spur-and-groove, isolated). Our design controlled for differences between individuals, diel and edge effects, and detection gaps resulting from the nonlinear relationship between acoustic tag detection probabilities as a function of distance from the receiver. We found red and black groupers preferred high-relief reef habitats, whereas mutton and yellowtail snappers preferred low-relief contiguous reef habitats. By identifying critical habitats for exploited species, our analysis may facilitate more efficient fishery-independent sampling and MPA designs.
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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 | 0.034 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".