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Record W6929885571 · doi:10.5061/dryad.v9s4mw6vs

Evaluation of four methods to identify the homozygotic sex chromosome in small populations

2022· dataset· en· W6929885571 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsHeterogametic sexGenomeLoss of heterozygosityChromosomeMicrosatelliteGenome ScanSex linkage

Abstract

fetched live from OpenAlex

Whole genomes are commonly assembled into a collection of scaffolds and often lack annotations of autosomes, sex chromosomes and, and organelle genomes (i.e., mitochondrial and chloroplast). As these chromosome types can have highly disparate evolutionary histories, it is imperative to take this information into account when analyzing genomic variation. Here we assessed the accuracy of four methods for identifying the homogametic sex chromosome using two whole genome sequenced (WGS) and 133 RAD sequenced white-tailed eagles (Haliaeetus albicilla): i) difference in read depth per scaffold, ii) heterozygosity per scaffold in a male and female bird, iii) mapping to a reference genome of a related species (chicken) with identified sex chromosomes, and iv) an analysis of SNP-loadings from a principal components analysis (PCA), based on low-depth RADseq data from 133 individuals. In i and ii, the WGS were mapped to a reference genome consisting of 1142 assembled scaffolds from the golden eagle (Aquila chrysaetos) with no identified chromosomes. The read depth per scaffold identified 86.41% of the homogametic sex chromosome (Z) with few false positives. The SNP-loading scores found 78.6% of the Z-chromosome but had a false positive discovery rate of more than 10%. The heterozygosity per scaffold did not provide clear results due to a lack of diversity in both the Z and autosomal chromosomes, and potential interference from the heterogametic sex chromosome (W).

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)0.018
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.001

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 head0.289
GPT teacher head0.496
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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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