Combined video–hydroacoustic survey of nearshore semi-pelagic rockfish in untrawlable habitats
Bibliographic record
Abstract
Abstract New survey technologies are needed to survey untrawlable habitats in a cost-effective and nonlethal manner with minimal impacts on habitat and nontarget species. Here, we test the efficacy of integrating data from a suspended underwater camera with acoustic data to generate population estimates for nearshore Black (Sebastes melanops), Blue (Sebastes mystinus), and Deacon Rockfish (Sebastes diaconus). We surveyed Seal Rock Reef near Newport, Oregon, and compared our results to population estimates derived from a mark–recapture study conducted at the same reef. We compared fish density estimates from video deployments to those calculated from applying published target strength to length regression models to our acoustics data. Densities derived from the acoustics, using a generalized physoclist target strength to length model, were significantly different from densities derived from video; conversely, a rockfish-specific target strength to length model generated densities that were not statistically different from video densities. To assess whether, and how, fish behaviour was influenced by the presence of an underwater camera, we deployed our camera under the acoustic transducer. No statistical difference was observed in the acoustic density of fish before, during, or after camera deployment. Our work suggests that combining acoustic and stereo video data provided a similar population estimate to historic survey results, but an accurate acoustic density estimate was dependent on using the proper acoustic target–strength model. We contend that combining camera data with hydroacoustic data is effective for surveying rockfish in untrawlable habitats.
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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 | 0.001 |
| 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".