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Record W4381612178 · doi:10.1139/as-2023-0014

Arctic cod (<i>Boreogadus saida</i>) in fjord and glacial habitats: a collaborative study with Uummannap Kangerlua fishers

2023· article· en· W4381612178 on OpenAlexaffvenueabout
Caroline Bouchard, Patrick Farnole, Kristine Lynge-Pedersen, Parnuna Petrina Egede Dahl, Henrik Christiansen

Bibliographic record

VenueArctic Science · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of VictoriaUniversité Laval
Fundersnot available
KeywordsFjordArcticFisheryFishingOceanographyGlacial periodHabitatHalibutCommercial fishingGeographyEnvironmental scienceEcologyFish <Actinopterygii>BiologyGeology

Abstract

fetched live from OpenAlex

Arctic cod ( Boreogadus saida) (Lepechin, 1774) is often found in front of glaciers, which is the least studied of the species’ habitats. Uummannap Kangerlua and Sullorsuaq in West Greenland provide a unique opportunity to study Arctic cod in the glacial habitat, as they are among the few places with a directed Arctic cod fishery. Inuit fishers from these fjords regularly catch Arctic cod as fresh bait for the Greenland halibut ( Reinhardtius hippoglossoides) (Walbaum, 1792) longline fishery, the main economic activity in the region. We collaborated with the fishers to learn about Arctic cod through interviews and collection of fish samples. Ten informants provided information on fishing areas, fishing methods, interannual variability in the catches, relationships with temperature and sea ice, and reported a spawning area near Saattut. One of the two samples collected contained only 21% males, which were longer, heavier, older, and had a higher gonadosomatic index than females. This skewed sex ratio may result from size-selective predation or spawning migration. Collaboration with fishers provided important baseline information on Arctic cod in fjord and glacial habitats. Continued efforts could bring a better understanding of key aspects of Arctic cod that are relevant for all Arctic communities located near glacial 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)0.001
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 score0.019
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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