Development of Spatial Operating Models to Test Survey Design and Calibrate a New Survey Index for Northwest Atlantic Fisheries Organization Subarea 0+1 (offshore) Greenland Halibut (Reinhardtius hippoglossoides)
Bibliographic record
Abstract
In 2018, the Paamiut research vessel used to conduct the bottom trawl survey in Baffin Bay and Davis Strait was retired, with no possibility to conduct comparative tows with the new Tarajoq research vessel. With the transition between vessels and trawl types generating a new index series starting in 2022, alternative methods that could be used to improve management advice for Greenland Halibut in the Northwest Atlantic Fisheries Organization Subarea 0+1 (offshore) is sought. This paper describes a framework for developing a spatial operating model used to simulate surveys and indices of abundance. Fishery and survey data were used to condition a population model and a spatiotemporal model to develop an operating model to describe historical abundance and distribution trends of Greenland Halibut (Reinhardtius hippoglossoides). Using the SimSurvey software package, the current random, depth-stratified survey design (in Divisions 0A-South and 1CD) was simulated and compared to an alternative, hypothetical survey that also included Division 0B. Simulation studies can identify the relative benefit of various components of survey design, e.g, additional survey coverage, strata boundaries, and set density, towards index precision within the logistical constraints of the survey, for example, ship time. The exploratory nature of this work was presented to identify models for potential assessment of this stock in the future. These approaches can address the survey gap between 2018-2021 and subsequent vessel change in two ways. First, the spatiotemporal model could be used to calibrate the catchability between the RV Paamiut and RV Tarajoq which would allow stitching the indices of abundance from the two vessels into a single time series. Initial exploration of model-based calibration with the 2019 survey on the FV Helga Maria detected lower catchability in some depth strata and size classes of fish, consistent with field observations. Further simulation testing and validation of the approach is recommended prior to broader use. Second, retrospective simulation through SimSurvey of an index of abundance, using catchability and selectivity estimates of the RV Paamiut and RV Tarajoq, can demonstrate if the two surveys can hypothetically provide similar inference on stock trends and magnitude over the same time period.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".