Diet composition of Steller sea lions (<i>Eumetopias</i> <i>jubatus</i>) in Frederick Sound, southeast Alaska: a comparison of quantification methods using scats to describe temporal and spatial variabilities
Bibliographic record
Abstract
We compared eight dietary indices used to describe the diet of Steller sea lions (Eumetopias jubatus (Schreber, 1776)) from 2001 to 2004 in Frederick Sound, southeast Alaska. Remains (n = 9666 items) from 59+ species categories were identified from 1684 fecal samples (scats) from 14 collection periods. The most frequently occurring prey were walleye pollock (Theragra chalcogramma (Pallas, 1814) = Gadus chalcogrammus Pallas, 1814; 95%), Pacific herring (Clupea pallasii Valenciennes in Cuvier and Valenciennes, 1847; 30%), Pacific hake (Merluccius productus (Ayres, 1855); 29%), and arrowtooth flounder (Atheresthes stomias (Jordan and Gilbert, 1880) = Reinhardtius stomias (Jordan and Gilbert, 1880); 21%). These species, along with Pacific salmon (genus Oncorhynchus Suckley, 1861) and skate (genus Raja L., 1758), accounted for 80%–90% of the reconstructed biomass and energy contribution, with pollock contributing 37%–60%. Overall, 80% of fish were 14–42 cm long and mainly pelagic, though 40% of scats contained benthic-associated prey. Steller sea lions switched from adult pollock to strong cohorts of juvenile pollock, and took advantage of spawning concentrations of salmon in autumn and herring in late spring and summer, as well as a climate-driven increase in hake availability. Observed temporal and site differences in diet confirm the need for robust long-term scat sampling protocols. All major indices similarly tracked key temporal changes, despite differences in occurrence and biomass-energy-based diet estimates linked to prey size and energy-density effects and the application of correction factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".