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Record W4300920214 · doi:10.47886/9781934874110.ch9

Pacific Salmon: Ecology and Management of Western Alaska’s Populations

2009· book-chapter· en· W4300920214 on OpenAlexaboutno aff

Bibliographic record

VenueAmerican Fisheries Society eBooks · 2009
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPeninsulaBayTributaryGeographyPopulationFisheryLocus (genetics)Chinook windMicrosatelliteOncorhynchusEcologyBiologyArchaeologyCartographyDemographyAllele

Abstract

fetched live from OpenAlex

Abstract.—Microsatellite and major histocompatibility complex (MHC) variation was surveyed to evaluate population structure and the potential for genetic stock identification in chum salmon Oncorhynchus keta populations largely bordering the eastern Bering Sea and northwestern Gulf of Alaska. Variation at 14 microsatellite loci and one MHC locus was surveyed for 59 populations in the study. The genetic differentiation index (Fst) over all populations and loci was 0.023, with individual locus values ranging from 0.007 to 0.058. At least 10 regional stocks were observed in the survey area, with populations from Kotzebue Sound (mean Fst value usually >0.02 in regional comparisons) and the Alaska Peninsula (Fst >0.03) the most distinct of Alaskan populations surveyed. For stock identification applications incorporating DNA variation, chum salmon sampled in rivers’ tributaries to the eastern Bering Sea were classified into the following regions: Kotzebue Sound, Norton Sound, lower/middle Yukon River, Tanana River, Kuskokwim River, Nushagak River, north/central Bristol Bay, southwest Bristol Bay, northern Alaska Peninsula/Aleutian Islands, southwest Alaska Peninsula, and southeast Alaska Peninsula. For simulated single-region mixtures, estimated regional stock compositions were generally above 90% for the previously-listed regions except for the Kuskokwim River, Nushagak and north/central Bristol Bay regions. Incomplete characterization of DNA variation for populations in those regions most likely contributed to the lower accuracies of estimated stock compositions in the simulated mixtures. Estimated regional stock compositions of simulated samples comprising fish from several regions were within 1–3% of actual values provided that no contributions from the three under-represented regions were included in the simulated samples. Microsatellite and MHC variation has the potential to provide accurate estimates of regional stock composition for chum salmon fisheries in the Bering Sea and northern Gulf of Alaska.

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)0.000
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 score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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