Estimating abundance indices for NAFO Subarea 2 + Division 3K redfish via a spatiotemporal model
Bibliographic record
Abstract
Redfish (Sebastes spp.) are commercially important groundfish whose fisheries experienced some devastating collapses in the Northwest Atlantic of Canada. The stock on the Labrador Shelf (Northwest Atlantic Fisheries Organization Subarea 2 + Division 3K) is currently under a fishing moratorium but has experienced some recent population growth. Since there is currently no accepted assessment model for this stock, crucial decisions on when and how the fishery may reopen are determined by abundance indices. One big concern for this stock is the reliability of the abundance indices estimated from research surveys, especially with the partial and inconsistent survey coverage that has occurred over time. A possible solution for index standardization is via a spatiotemporal model, which utilizes spatial and spatiotemporal correlations to estimate trawl catches in unsampled areas based on sampled catches from neighbouring areas and years. In Chapter 1 I provide an overview of the redfish fishery and background on index standardization. In Chapter 2 I test different temporal and spatiotemporal structures for a model and use the best-performing model to predict catches in unsampled areas and therefore fill the gaps in survey coverage. I also quantify the uncertainties due to these predictions and generate new standardized survey abundance indices for the stock. There is also the possibility in the future of reductions in survey efforts in many regions in Subarea 2 + Division 3K, especially the near-shore and deep-water strata. Therefore, in Chapter 3, I test various survey configurations to assess the consequences of further reduction in sampling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".