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Record W3156040165 · doi:10.11646/zootaxa.4963.2.5

Cryptic diversity across the Trans-Mexican Volcanic Belt of Mexico in the montane bunchgrass lizard Sceloporus subniger (Squamata: Phrynosomatidae)

2021· article· en· W3156040165 on OpenAlexaff
Robert W. Bryson, Jared A. Grummer, Elizabeth M. Connors, JOSEPH TIRPAK, John E. McCormack, John Klicka

Bibliographic record

VenueZootaxa · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSquamataBiologyTussockMontane ecologyEcologySpecies complexGenetic diversityLizardDNA barcodingPopulationCloud forestZoologyPhylogenetic treeDemography

Abstract

fetched live from OpenAlex

Sceloporus subniger Poglaygen Smith is a montane bunchgrass lizard distributed across pine-oak forests of central Mexico. Prompted by the discovery of a new population of this lizard in far western Mexico, and by recent studies suggesting S. subniger may be a composite of several distinct species, we examined in more detail the genetic structure of S. subniger. We generated a mitochondrial DNA (mtDNA) dataset from 81 specimens and an ultraconserved elements (UCE) dataset representing thousands of genomic regions from 12 specimens to specifically evaluate the genetic distinctiveness of populations from western Michoacán and adjacent Jalisco along with the newly discovered population in the Sierra de Mascota in western Jalisco. We also recorded morphological data from 47 museum specimens to compare to our genetic data. Results from our analyses of the genetic data, augmented by specimen measurements and scale counts, support the notion that S. subniger is indeed a composite of distinct species. Montane bunchgrass lizards from western Michoacán and adjacent Jalisco, and from the Sierra de Mascota in western Jalisco, each represent distinct new species, which we describe and name here.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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.

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

Citations11
Published2021
Admission routes1
Has abstractyes

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