Modeling of the spatial distribution of Pacific spiny dogfish (<i>Squalus suckleyi</i>) in the Gulf of Alaska using generalized additive and generalized linear models
Bibliographic record
Abstract
The Pacific spiny dogfish (Squalus suckleyi) is a common bycatch species in the Gulf of Alaska. Their spatial distribution is poorly understood, as most catch is discarded at sea. We analyzed spiny dogfish spatial distribution from fishery-dependent and -independent observations of longline gear between 1996 and 2008 using generalized additive and generalized linear models. Poisson, negative binomial, and quasi-Poisson error structures were investigated; the quasi-Poisson generalized additive model fit best. Models showed that spiny dogfish catches were concentrated east of Kodiak Island in waters ≤100 m deep. Results facilitate design of future spiny dogfish assessment surveys and identification of areas in which to focus at-sea observations for fishing mortality estimation, and provide the basis for first-ever designation of spiny dogfish essential fish habitat, despite US legal requirements for essential fish habitat designations since 1996. Identified areas of high bycatch may expedite spatial management by indicating areas in which directed spiny dogfish fisheries could be focused or, conversely, areas in which heightened conservation and catch accounting efforts would be most effective to prevent overfishing of this long-lived, late-maturing species.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".