Interactions between jumbo squid (Dosidicus gigas) and Pacific hake (Merluccius productus) in the northern California Current in 2007
Bibliographic record
Abstract
During a joint Canada-U.S. Pacific hake (Merluccius productus) acoustic-trawl survey in 2007, 82 jumbo squid (Dosidicus gigas) were captured at depths exceeding 300 m offshore of the continental shelf along Vancouver Island and the Queen Charlotte Islands. Because the acoustic signs associated with these captures were unusual, we compared 38 kHz echograms collected during trawls in which both hake and jumbo squid were caught with those from nearby trawls in which only hake or squid were caught.Hake appeared to be more widely dispersed or less densely aggregated when jumbo squid were captured concurrently.We hypothesize that squid predation causes an avoidance response in hake, thereby altering normal aggregation behavior. Although our evidence of jumbo squid predation on Pacific hake is limited to seven echogram comparisons, this new predator-prey interaction may lead to cascading trophic impacts in the northern California Current.On a practical level, our findings also suggest that the acoustic survey methods, which use a combination of visual echogram interpretation and trawling to verify target identification, will require adjustment. If hake are dispersed over larger coastal areas or do not aggregate as recognizable targets when jumbo squid are present, then additional ship time and other resources may be required for future acoustic-trawl surveys.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".