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Record W2066874935 · doi:10.1139/f09-091

Polyploid microsatellite data reveal stock complexity among estuarine North American green sturgeon (Acipenser medirostris)

2009· article· en· W2066874935 on OpenAlexvenueno aff
Joshua A. Israel, K. Jun Bando, Eric C. Anderson, Bernie May

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersGuangdong Academy of Sciences
KeywordsSturgeonEstuaryAcipenserBayFisheryPopulationMicrosatelliteGeographyBiologyEcologyAllele

Abstract

fetched live from OpenAlex

North American green sturgeon ( Acipenser medirostris ) display the distinctive behavior of long ocean migrations along the west coast punctuated by summer residence in estuaries; however, little is known about stock composition in these tidal environments. Pairwise comparisons and genetic clustering analysis were used to characterize the two green sturgeon Distinct Population Segments (DPSs) from 20 collections using eight tetrasomic and two disomic microsatellite loci. The observed pattern of green sturgeon DPS composition among five estuaries in California, Oregon, and Washington was supported with assignment testing approaches utilizing the same genotypic data in codominant polysomic and pseudodominant allele phenotype formats. The majority of individuals in northern DPS estuaries originated from the threatened Southern DPS, except in Winchester Bay and Grays Harbor. We detected few Northern DPS green sturgeon in San Pablo Bay, the principal Southern DPS estuary, supporting that green sturgeon preferentially disperse north once they enter their coastal migration. Our genetic findings suggest that stock complexity in green sturgeon is pervasive and support precautionary, interjurisdictional approaches for managing green sturgeon beyond rigid, regulatory boundaries.

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 score0.995
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.037
GPT teacher head0.239
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

Citations40
Published2009
Admission routes1
Has abstractyes

Explore more

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