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Record W2952997036 · doi:10.3996/122018-jfwm-118

Is Dolly Varden in Arctic Alaska Increasing in Length in a Warming Climate?

2019· article· en· W2952997036 on OpenAlexaboutno aff
Michael B. Courtney, Harrison DeSanto, Andrew C. Seitz

Bibliographic record

VenueJournal of Fish and Wildlife Management · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsSalvelinusArcticSubsistence agricultureFish migrationGeographyEcologyClimate changeArctic ecologyFisheryBeaufort seaBiologyHabitat

Abstract

fetched live from OpenAlex

Abstract The body condition, abundance, and size of several vertebrate taxa occupying the Alaskan and Canadian Arctic have increased in this rapidly changing environment. Presently, anecdotal stakeholder reports suggest that anadromous populations of Dolly Varden Salvelinus malma in Arctic Alaska are attaining greater maximum sizes than reported in the past. However, growth analyses have not been conducted for any substantial period of time. To qualitatively examine one facet of growth, we reviewed scientific journal articles, gray literature, and unpublished data for reported maximum lengths of Dolly Varden from the Chukchi and Beaufort seas collected over the past ∼50 y (1969–2015). Regression analyses of maximum length of Dolly Varden from 1969 to 2015 support the observations that the maximum size of this species is likely increasing in a changing Arctic. These results, coupled with the lack of comprehensive growth data, highlight the importance of long-term monitoring of organismal responses to a changing environment and provide valuable direction for future research on this important subsistence resource for Indigenous peoples who inhabit the Arctic.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.143
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.021
GPT teacher head0.321
Teacher spread0.300 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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