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Record W2913774439 · doi:10.3390/f10020101

Diversity and Genetic Structure Inferred with Microsatellites in Natural Populations of Pseudotsuga menziesii (Mirb.) Franco (Pinaceae) in the Central Region of Mexico

2019· article· en· W2913774439 on OpenAlexaboutno aff
Paulina Montiel Castelán, Moises Cortés-Cruz, Ma. del Carmen Mendoza-Castillo, Serafín Cruz-Izquierdo, Javier López-Upton, Isaac Sandoval-Padilla, Vidal Guerra de la Cruz

Bibliographic record

VenueForests · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsnot available
Fundersnot available
KeywordsAnalysis of molecular varianceGenetic diversityBiologyMicrosatelliteGenetic variationPinaceaeGenetic structureEvolutionary biologyLoss of heterozygosityLocus (genetics)AlleleBotanyGeneticsPopulationDemographyGene

Abstract

fetched live from OpenAlex

The amount and structure of the genetic diversity in Mexican populations of Pseudotsuga menziesii (Mirb.) Franco, is almost unknown, since most genetic studies have been carried out on populations from Canada and the United States. Here, we applied a set of 12 microsatellite markers to 12 populations (234 trees) from the central region of Mexico in order to determine values of genetic diversity and differentiation. Seventy-three different alleles were identified: an average number of alleles per locus (Na) of 6.083, effective number of alleles (Ne) of 2.039, observed heterozygosity (Ho) of 0.229, and expected heterozygosity (Ht) of 0.417. Genetic differentiation was high: the coefficient of differentiation (θ) was 0.270, while the coefficient of structure (Φst) was 0.278. Bayesian analysis identified two genetic groups in central Mexico. The PCoA and the dendrogram were in concordance with the two genetic groups. The results of the analysis of molecular variance (AMOVA) indicate that genetic variation exists mainly within populations (72.149%). Therefore, conservation efforts should focus on as many individuals within populations as possible, to maintain this variation.

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 score0.006
Threshold uncertainty score0.283

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 head0.010
GPT teacher head0.216
Teacher spread0.207 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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