A spatial analysis of longline survey data for improved indices of Atlantic halibut abundance
Bibliographic record
Abstract
Abstract Atlantic halibut (Hippoglossus hippoglossus) support an economically important fishery on the eastern coast of Canada. Like other species that are not well sampled by trawl surveys, halibut in this area are monitored using longline surveys. These surveys present challenges that can make obtaining indices of abundance difficult. Issues include gear saturation, which can result in a non-linear relationship between catch per unit effort and local abundance. The current approach to obtain a relative index consists of fitting a multinomial exponential model to a subset of hooks from each survey station. While this approach accounts for hook competition, it does not account for the presence of spatial patterns. We therefore extend the multinomial exponential model to include spatial random fields for both Atlantic halibut and non-target species, set-specific soak time, and data from the hooks. Furthermore, we propose a method for aggregating the resulting spatially varying indices to obtain an annual index for the entirety of the modelled area. This novel approach identifies Atlantic halibut hotspots in multiple years, while simultaneously providing relative abundance indices for 2017 through 2020. These outcomes demonstrate the widespread applicability of our methods for improving the scientific advice upon which fisheries management decisions are based.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".