Squid Abundances and Relevance, Gulf of Alaska Expeditions 2019 and 2020
Bibliographic record
Abstract
Squid is a major diet item of Pacific salmon (Oncorhynchus spp.) in offshore waters, especially for coho (O.kisutch), Chinook (O.tshawytscha), and steelhead (O.mykiss) (Davis et al. 1998;Aydin 2000;Davis 2003;Kaeriyama et al. 2004).Despite their importance, relatively little is known about squid populations on the high seas, including their distribution, life cycle, population structure, spawning areas, and movement at different life stages relative to ocean currents.After a proposal to include winter surveys in salmon studies (Beamish 2012), winter expeditions took place in 2019 and 2020 in the Gulf of Alaska (GoA); the ultimate goal was to discover the fundamental mechanisms that regulate salmon in the North Pacific Ocean.One of the objectives was to study squid abundance, composition and condition in the upper epipelagic layer.Methodology and some preliminary results are described in Pakhomov et al. (2019) and Somov et al. (2020).During both winter expeditions squid was an important component in coho, Chinook, and steelhead diet (Fig. 1); this finding reinforced the need of squid studies in relation to salmon.Here we present a summary on squid abundance, distribution, and relevance based on 64 epipelagic (0-30 m) trawl catches, 60 Juday plankton nets (0-250 m) in 2019 and 52 epi-pelagic (0-20 m) trawl catches, 49 Juday plankton nets (0-200 m) in 2020.We also present preliminary results of squid detected in environmental DNA (eDNA) analysis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".