Simultaneous Bayesian estimation of size-specific catchability and size spectrum parameters from trawl data
Bibliographic record
Abstract
Abstract Fisheries-independent surveys are a critical tool for monitoring marine populations and communities. However, considerations must be made to account for variable-size-based catchability. The size-specific catchability function is therefore key for estimating size distributions, but often requires extensive data sets or specialized field experiments to determine. We develop a Bayesian model capable of simultaneously estimating both a size-based catchability curve and species-specific size spectrum parameters from trawl data by assuming that individual species size spectra follow a theoretically derived parametric size spectrum model. The resulting model provides a means of estimating catchability and size spectra within an adaptive framework capable of accommodating confounding factors such as vessel power and fish density, potentially allowing for improved biomass and productivity estimates. We demonstrate the application of this model using 15 years of Greenland Halibut (Reinhardtius hippoglossoides) survey data from Nunavut to determine size-specific catchabilities and assess whether the size spectrum of Greenland Halibut has changed across the time series. While size spectrum parameters for this stock were not found to vary, we did find evidence of time-varying catchability parameters across the study period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".