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Record W3092041118 · doi:10.1139/as-2019-0005

Size class segregation of Arctic cod (<i>Boreogadus saida</i>) in a shallow High Arctic embayment

2020· article· en· W3092041118 on OpenAlexafffundvenueabout
Steven T. Kessel, Richard E. Crawford, Nigel E. Hussey, Silviya V. Ivanova, Jeremy P. Holden, Aaron T. Fisk

Bibliographic record

VenueArctic Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsMinistry of Natural Resources and ForestryUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBayArcticShoalFisheryOceanographyEnvironmental scienceAbundance (ecology)Fish <Actinopterygii>HabitatPredationGeographySpatial distributionEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Arctic cod (Boreogadus saida (Lepechin, 1774)) vertically segregate by size class in deep waters, but such dynamics had not been explored in shallow waters. Spatial distribution of Arctic cod was investigated in Resolute Bay, Nunavut, Canada (74°41N, 94°52W) from 20 July to 5 August 2012, using a combination of hydroacoustic survey and direct capture. Hydroacoustic surveys identified two high concentrations of Arctic cod, with larger individuals detected on the west side, and smaller individuals on the east. Catch data confirmed size segregation, with fish sampled on the west side of the bay significantly larger (mean = 174 mm total length (TL); 35.9 g weight (WT)) than those on the east (mean = 110 mm TL; 9.2 g WT). Fish density on the west was estimated at 3.52 fish·m −2 , extrapolated to the full 0.52 km 2 of the surveyed shoal to ∼1 830 400 fish and 65 711 kg (assuming a 35.9 g mean WT). Smaller fish on the east side were more abundant (9.32 fish·m −2 ; total abundance ∼11 836 400 fish or 108 894 kg; mean WT = 9.2 g). Horizontal habitat-partitioning was observed between Arctic cod size classes over a small geographic area (∼8 km 2 ), most probably due to partitioned resources and to mitigate predation risk.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.016
GPT teacher head0.239
Teacher spread0.223 · 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.

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

Citations3
Published2020
Admission routes4
Has abstractyes

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