Management of Dosidicus gigas, a large, pelagic predator in the eastern North Pacific Ocean
Bibliographic record
Abstract
The Humboldt squid ( Dosidicus gigas ) has been expanding its geographical range in the eastern North Pacific Ocean over the past 20 years. This species of squid has advanced from the most southern part of their native range, off the Chilean coast, northward to southern Alaska. This expansion of a fast-growing pelagic predator is concerning and should be evaluated. Dosidicus gigas has been known to negatively affect native species of fish populations, such as Pacific hake (Merluccius productus), when expanding its range. The effects of an establishment of a D. gigas population in Pacific Canadian waters on both ecological systems as well as commercial fisheries should be assessed to develop management plans to protect native species and commercial fisheries. The reduction of potential effects of D. gigas establishment on native species of the eastern North Pacific is essential to conserving current native populations. A complete list of trophic interactions between D. gigas and species native to the eastern North Pacific is still underdeveloped. To qualitatively assess what is driving the migratory behaviour of D. gigas , the physiological, reproductive, and ecological traits of the species are reviewed here. The results indicate that warming water temperatures, reproductive plasticity, and prey/predator interactions are the leading causes thought to be driving D. gigas northward.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".