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Record W2008117726 · doi:10.1016/j.icesjms.2005.05.008

Capelin (Mallotus villosus) distribution and climate: a sea “canary” for marine ecosystem change

2005· article· en· W2008117726 on OpenAlexafffund
George A. Rose

Bibliographic record

VenueICES Journal of Marine Science · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapelinMallotusClimate changePelagic zoneForage fishEcologyFisheryBorealMarine ecosystemEcosystemBiologyGeographyPredation

Abstract

fetched live from OpenAlex

Abstract Capelin (Mallotus villosus) is a classic “r” adapted pelagic species that inhabits the northern boreal oceans at the margins of cold Arctic waters. The species originated in the North Pacific and colonized the North Atlantic at least once during interglacial periods of the past few million years. Capelin became the main forage species for many larger predatory fish, and also for seabirds and marine mammals. The colonizing abilities of capelin have been noted in historical anecdotes, typically in concert with climate variations. In this paper, all known shifts in distribution are catalogued. Shifts have taken place at the larval and adult stages, and some result in new spawning locations, others do not. Displacement distance relates to temperature change: log10(distancekm) = 0.28 × temperature change + 2.16 (p < 0.05, r2 = 0.91). The persistence of the shifts relates to the displacement distance: log10(persistencey) = 2.62 × log10(distancekm) − 6.56 (p < 0.05, r2 = 0.83). The quick and consistent response of capelin to temperature change, its importance to the North Atlantic foodweb, and established monitoring methods suggest this species as a sea “canary” for northern boreal marine ecosystem responses to climate variability and change.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations138
Published2005
Admission routes2
Has abstractyes

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