Spatial reference points for groundfish
Bibliographic record
Abstract
Abstract According to density-dependent habitat selection theory, areas of high density can be indicative of high population productivity and have positive individual fitness consequences. Here, we explore six groundfish populations on the Scotian Shelf, Canada, where a decline in areas of high density beyond a certain threshold is associated with disproportionately large declines in Spawning Stock Biomass (SSB). This is evidenced by empirical, concave, positive relationships between high-density areas (HDAs) and SSB. We introduce a methodology to estimate the threshold below which SSB declines increasingly faster per unit of HDA decline. The spatial threshold among these six stocks was remarkably consistent; when stocks lose 70–80% of HDAs, disproportionately large SSB declines are likely to occur. We propose that spatial thresholds could serve as spatial reference points to complement existing SSB limit reference points (LRPs). For some stocks we identify spatial thresholds which correspond to SSB levels that exceed those associated with the designated SSB LRP, suggesting that a review of these SSB LRPs warrants merit. For other stocks, spatial reference points can be used in concert with SSB reference points, strengthening efforts to incorporate a precautionary approach to fisheries management. Our results warrant further research into the general application of HDA as spatial limit and target reference points for fisheries management in addition to other population status indicators within a broad recovery framework.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".