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Record W3142851183 · doi:10.1139/as-2019-0037

The comparative lake ecology of two allopatric Arctic Charr, <i>Salvelinus alpinus</i>, populations with differing life histories in Cumberland Sound, Nunavut

2021· article· en· W3142851183 on OpenAlexaffvenueabout
Angela L. Young, Ross F. Tallman

Bibliographic record

VenueArctic Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaUniversity of Manitoba
Fundersnot available
KeywordsSalvelinusFish migrationArcticEcologyHabitatBiologyFisheryGeographyFish <Actinopterygii>Trout

Abstract

fetched live from OpenAlex

The lake ecology of high-latitude fishes is strongly influenced by seasonal feeding opportunities and environmental stochasticity in Arctic environments. Arctic Charr (Salvelinus alpinus (Linnaeus, 1758)) populations are prevalent throughout the Arctic and show multiple life history strategies across their range. Unlike Old World populations, the lake ecology of Arctic Charr populations on south Baffin Island remains poorly defined. We examined the comparative seasonal lake ecology of two differing Arctic Charr populations (anadromous and landlocked) in Cumberland Sound, Nunavut. Anadromous Charr showed no evidence of feeding occurring within freshwater once they began seaward migrations. Anadromous Charr achieve sexual maturation at a larger size and younger age than landlocked Charr. Landlocked Charr used more lake habitats than anadromous Charr with feeding opportunities as an apparent influence on habitat selection. Landlocked Charr fed year round. They adopted a cannibalistic feeding strategy in the winter but consumed a variety of prey items in the fall. Littoral habitat was found to be important to all sizes of Charr in both seasons. Smaller anadromous Charr (&lt;350 mm) did not use the benthic habitat. The variable ecology and form demonstrated further emphasizes the phenotypic adaptability of Arctic Charr that allows its widespread distribution in the Arctic.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.265
Teacher spread0.241 · 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.

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

Citations7
Published2021
Admission routes3
Has abstractyes

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