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Record W2134780678 · doi:10.1525/cond.2008.8475

CONCORDANT AND DISCORDANT SIGNALS BETWEEN GENETIC DATA AND DESCRIBED SUBSPECIES OF PACIFIC COAST SONG SPARROWS

2008· article· en· W2134780678 on OpenAlexaff
Christin L. Pruett, Peter Arcese, Yvonne L. Chan, Amy Wilson, Michael A. Patten, Lukas F. Keller, Kevin Winker

Bibliographic record

VenueOrnithological Applications · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSubspeciesBiologyMicrosatelliteEvolutionary biologyConcordanceGeographic variationGenetic variationZoologyPopulationGeneticsDemographyGene

Abstract

fetched live from OpenAlex

Song Sparrows (Melospiza melodia) provide one of North America's best examples of geographic variation in phenotype, with approximately 26 described subspecies recognized. However, researchers have found inconsistent signals when making comparisons between subspecies and genetic markers. We examined seven microsatellite loci from 576 Song Sparrows of 23 western North American populations representing 13 recognized subspecies. We assessed the level of concordance between microsatellite genotypes and subspecies. We found that in some, but not all, instances neutral genetic structure corresponded to recognized phenotypic structure. However, some populations not currently recognized as subspecies were found to be genetically differentiated from all other populations that are considered to be the same subspecies. We suggest that a combination of phenotypic characters, behavioral traits, and multiple loci be used when assessing geographic variation in birds, and that sampling should be conducted in more than one location within broadly distributed subspecies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.077
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.064
GPT teacher head0.268
Teacher spread0.204 · 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.

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

Citations18
Published2008
Admission routes1
Has abstractyes

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