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Record W4387080737 · doi:10.1139/as-2023-0022

Characterization of skin- and intestine microbial communities in migrating High Arctic lake whitefish and cisco

2023· article· en· W4387080737 on OpenAlexafffundvenueabout
Erin F. Hamilton, Collin L. Juurakko, Katja Engel, Peter van Coeverden de Groot, John M. Casselman, Charles W. Greer, Josh D. Neufeld, Virginia K. Walker

Bibliographic record

VenueArctic Science · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsNational Research Council CanadaUniversity of WaterlooMcGill UniversityQueen's University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaQueen's UniversityGovernment of CanadaGenome CanadaOntario GenomicsNunavut Arctic CollegeOntario Genomics InstitutePolar Knowledge CanadaEuropean Bioinformatics Institute
KeywordsCoregonus clupeaformisFisheryCoregonusArcticBiologyEcologyFishingGeographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

At high latitudes, lake whitefish ( Coregonus clupeaformis) and others in the closely related Coregonus species complex (CSC) including cisco ( C. autumnalis and C. sardinella) can be diadromous, seasonally transitioning between freshwater lakes and the Arctic Ocean. CSC skin- and intestine microbiomes were collected, facilitated by Inuit fishers at sites on and around King William Island, Nunavut, at the northern range limits of lake whitefish. Community composition was explored using 16S rRNA gene sequencing and microbiota distinctly grouped depending on fishing site salinity. Overall, lake whitefish intestine communities were more variable than those of the two cisco with higher Shannon diversity, suggesting that lake whitefish and their microbiomes could be susceptible to environmental stress possibly leading to dysbiosis. Lake whitefish showed lower condition (K) in the ocean than in freshwater rivers, whereas cisco condition was similar among distinct seasonal habitats. Taken together, the impact of changing habitats on fish condition and microbial composition may inform approaches to CSC health in fisheries and aquaculture, in addition to being relevant for northern Indigenous peoples with subsistence and economic interests in these resources.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.037
GPT teacher head0.331
Teacher spread0.295 · 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

Citations0
Published2023
Admission routes4
Has abstractyes

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