Modeling the distribution of thorny skate (<i>Amblyraja radiata</i>) in the southern Grand Banks (Newfoundland, Canada)
Bibliographic record
Abstract
Globally, commercial fisheries have had a strong impact on elasmobranch populations directly through high catch rates and indirectly through bycatch. Consequently, the abundance of many species is declining to the extent that some are considered under threat of extinction. Regionally, this negative trend is also evident in the international waters of the southern Grand Banks (off the coast of Newfoundland, Canada), where the occurrence of the thorny skate (Amblyraja radiata) has declined by nearly 70% in recent decades. This study used Bayesian species distribution models to investigate the habitat preference and biomass trends of the thorny skate during a 14-year period (2003–2017), linking five environmental predictors (i.e., bathymetry, sea bottom temperature, seabed aspect, slope, and rugosity) and prey distribution with fishery-independent data. Our findings identify some of the sensitive habitats for this species and the ecological factors that may be driving its population dynamics in the area. We argue that knowledge about the factors influencing the distribution of this species and spatiotemporal effects could be exploited as potential mitigation measures for future fishery management strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".