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Record W2300701093 · doi:10.14430/arctic4543

Systematic Review and Critique of the Contributions of Traditional Ecological Knowledge of Beluga Whales in the Marine Mammal Literature + Supplementary Appendix Table (See Article Tools)

2016· article· en· W2300701093 on OpenAlexfundvenueaboutno aff
Kaitlin Breton‐Honeyman, Chris Furgal, Michael O. Hammill

Bibliographic record

VenueARCTIC · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaTrent UniversityArcticNet
KeywordsBeluga WhaleBelugaMarine mammalGeographyFisheryTraditional knowledgeTable (database)DisciplineDocumentationIndigenousLibrary scienceEcologySociologyArcticBiologySocial scienceComputer science

Abstract

fetched live from OpenAlex

In this study we systematically review and critique literature containing Traditional Ecological Knowledge (TEK) of the beluga (<em>Delphinapterus leucas</em>) as a case study to gain insights into TEK’s contributions to the marine mammal literature over the past four decades. We reviewed multiple searchable online databases, collected both academic and grey literature, and categorized it by geographic and disciplinary focus, as well as by the contribution of TEK to the source. Of the total 137 papers retained in the final analysis, 67% referred to the Canadian North, particularly the Hudson Bay subregion. Articles that included informal or anecdotal representations of TEK of belugas were the most prevalent. The number of papers containing TEK of belugas increased rapidly between 1975 and 2004 but appears to have leveled off since then. Biological papers represented the largest disciplinary focus (72%), followed by papers on management or co-management. This review showed that although knowledge of Indigenous peoples has made substantial contributions to the understanding of beluga, there is a lack of explicit collection, documentation, and use of TEK in the literature on belugas and particularly in the literature on beluga management.

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 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 score0.113
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.250
Teacher spread0.234 · 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

Citations22
Published2016
Admission routes3
Has abstractyes

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