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Record W7101051407

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2007· article· en· W7101051407 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicBody Image and Dysmorphia Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPredationPredatorForage fishForageFish stockFish <Actinopterygii>Abundance (ecology)Smelt
DOInot available

Abstract

fetched live from OpenAlex

The impact of marine mammals, particularly seals, on the recovery of depleted fish stocks is a controversial issue and the focus of significant research efforts. Three species of seals are considered important predators in the northwest Atlantic, harp, hooded and grey seals. Harp and hooded seals are seasonal migrants that have shown little or no increase in abundance over the past decade. Grey seals are residents of temperate waters that, after a number of decades of exponential growth, are beginning to show signs of density dependent reductions in growth rates. Consumption of important prey species by seals Atlantic Canada has been estimated using bioenergetics models. Harp seals are important predators in Divisions 2J3KL and 4RS while grey seals are the most important pinniped predator in 4T and 4VsW. Hooded seals feed primarily in 2J3KL and 3M. Recent advances in methods of estimating diet have provided new insights into the importance of individual prey species that will require new estimates of consumption. A number of studies have attempted to determine the impact of seals on fish stocks in the northwest Atlantic, particularly the impact of harp and/or grey seals on Atlantic cod. In general, these studies have indicated that although seals consume substantial amounts of commercial fish species and important forage species, the impact of these removals on the current fish stocks is difficult to determine. Seals are important predators of both large and small cod and could be playing a role in the non-recovery of cod stocks, but seal predation can not account

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.829
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.015
GPT teacher head0.336
Teacher spread0.321 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2007
Admission routes1
Has abstractyes

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