Estimation of the capture efficiency and abundance of Atlantic sea scallops (<i>Placopecten magellanicus</i>) from paired photographic–dredge tows using hierarchical models
Bibliographic record
Abstract
The efficiency of survey gear is an important measure that can be used to estimate the absolute scale of populations in assessment models. We develop a general hierarchical model for estimating the efficiency of a New Bedford-style sea scallop (Placopecten magellanicus) survey dredge from paired dredge and Habcam camera system tows. Habcam data for each tow consist of multiple images that give information on within-tow variability of scallop density. The model accounts for these multiple observations as well as the possibility of differences between the true densities in dredge and Habcam pairs due to the pairs not covering exactly the same ground. We fit several models with alternative assumptions to observations on Atlantic sea scallops and compare the relative performance using Akaike’s information criterion. The best performing model estimated higher dredge efficiency on fine substrates than on coarse ones (approximately 0.40 and 0.27, respectively). Our results inform the scale of annual abundance estimates from dredge surveys and reduce uncertainty in the sea scallop stock assessments.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".