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Record W4402558736 · doi:10.1139/cjfas-2024-0053

Marine migration, thermal habitat use and feeding habits of Arctic charr ( <i>Salvelinus alpinus</i> ) in SW Greenland

2024· article· en· W4402558736 on OpenAlexafffundvenue
Jan Grimsrud Davidsen, Sindre Håvarstein Eldøy, Adam T. Piper, Coralie Moccetti, Jakob Brodersen, Frederick G. Whoriskey, Michael Power

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsUniversity of WaterlooOcean Tracking NetworkDalhousie University
FundersDalhousie UniversityCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungZoological Society of LondonNorges Teknisk-Naturvitenskapelige Universitet
KeywordsSalvelinusArcticHabitatFisheryEcologyBiologyArctic charGeographyOceanographyFish <Actinopterygii>ZoologyTroutGeology

Abstract

fetched live from OpenAlex

Climate change is altering northern coastal aquatic habitats, especially in fjords. Data on current ecosystem structure and biodiversity in many northern fjord and coastal ecosystems, especially for Greenland, are lacking. We used acoustic telemetry combined with stable isotope analyses in a southwest Greenland fjord to investigate marine migrations, marine, and freshwater thermal habitat use, and the marine feeding habits of 80 acoustically tagged Arctic charr over one year. During summer, most Arctic charr occupied the inner fjord. Models of Arctic charr thermal habitat use suggested higher experienced water temperatures in the inner compared to outer fjord (estimated 1.59 °C difference) during tagged charr mean 70-day (SD = 14 days) residencies. During February and March, non-migratory individuals used warmer waters (+0.56 °C higher) than fish that ultimately migrated to sea, suggesting that over-wintering habitat use patterns influenced migration tactics. Stable isotope mixing model analysis indicated that Arctic charr fed mainly on capelin, marine gammarids, and sandlance. The results provide a contemporary baseline for assessing predictions of potential changes in the ecology of Arctic charr in SW Greenland fjords.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.990

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)0.0000.000

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 head0.033
GPT teacher head0.209
Teacher spread0.176 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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