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Record W2188833805 · doi:10.47536/jcrm.v11i3.609

Subsistence harvest of beluga or white whales (Delphinapterus leucas) in northern and western Alaska, 1987–2006

2023· article· en· W2188833805 on OpenAlexaboutno aff
Kathryn J. Frost, Robert Suydam

Bibliographic record

Venue˜The œjournal of cetacean research and management. Special issue · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersNational Marine Fisheries ServiceAlaska Department of Fish and GameNational Oceanic and Atmospheric AdministrationMassachusetts Department of Fish and Game
KeywordsBeluga WhaleBelugaBayFisheryBeaufort seaSubsistence agricultureLeucasGeographyOceanographyBeaufort scaleStock (firearms)ArcticBiologyGeologyArchaeology

Abstract

fetched live from OpenAlex

Four stocks of beluga or white whales (Delphinapterus leucas) are hunted by Alaska Natives in northern and western Alaska. These are the Beaufort Sea, eastern Chukchi Sea, eastern Bering Sea and Bristol Bay stocks. Since 1987, the Alaska Beluga Whale Committee has monitored the subsistence harvests of belugas from these stocks. During this 20 year period, the total landed harvest for the four stocks combined (adjusted for years with missing data) ranged from 208 in 1995 to 494 in 1988, with a 20 year average of 323 per year. For individual stocks the average annual landed harvests for 1987–2006 were: Beaufort Sea – 41; Chukchi Sea – 62; eastern Bering Sea – 191; and Bristol Bay – 20. There was no significant longterm trend (p>0.05) in the rate of harvesting for any stock from 1987–2006. Average landed harvests relative to estimated stock size were: 0.1% for the Beaufort Sea (0.4% including belugas harvested from the Beaufort Sea stock by Canadian hunters); 1.7% for the eastern Chukchi Sea; 1.1% for the eastern Bering Sea; and 1.1% for Bristol Bay. The success of beluga harvest monitoring in Alaska is due to the cooperation of beluga hunters from more than 40 small coastal communities who report their harvests to the Alaska Beluga Whale Committee (ABWC). Through the ABWC, beluga hunters have been able to formalise their role in managing their subsistence 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.152
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.313
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2023
Admission routes1
Has abstractyes

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