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Record W2117657582 · doi:10.1139/z00-060

Pollock and the decline of Steller sea lions: testing the junk-food hypothesis

2000· article· en· W2117657582 on OpenAlexvenueno aff
David A. S. Rosen, Andrew W. Trites

Bibliographic record

VenueCanadian Journal of Zoology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsPollockHerringPacific herringBiologyFisheryFish <Actinopterygii>Clupea

Abstract

fetched live from OpenAlex

The decline of Steller sea lions (Eumetopias jubatus) in the Gulf of Alaska and the Aleutian Islands may be the result of them eating too much pollock (a gadid fish) instead of a more balanced and diverse diet containing fattier fishes, such as herring or sandlance. We sought to test this junk-food hypothesis by feeding six captive Steller sea lions (ages 0.9-4.5 years) only pollock or herring. All sea lions gained mass while eating herring. However, eating only pollock for short periods (11-23 d) caused the study animals to lose an average of 6.5% of their initial body mass (0.6 kg/d) over an average feeding trial of 16 d (initial mass averaged 125 kg). The animals were allowed to eat as much pollock as they wanted but did not increase their food intake to compensate for the low energy they were receiving. The sea lions showed progressive metabolic depression while losing body mass on a pollock-only diet. The loss of body mass while eating pollock was due to the lower gross energy content of pollock versus herring, the higher cost of digesting pollock, and the increased energy loss from digesting the larger quantity of fish needed to compensate for the lower energy content of pollock. Thus, our sea lions would have had to eat 35-80% more pollock than herring to maintain similar net energy intakes. Results from our captive-feeding studies are consistent with the junk-food hypothesis and have serious implications for Steller sea lions that have been eating primarily pollock in the Gulf of Alaska and the Aleutian Islands.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.030
GPT teacher head0.204
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations133
Published2000
Admission routes1
Has abstractyes

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