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Record W4394542899 · doi:10.6084/m9.figshare.17711334

<i>TAS2R16</i> introgression from banteng into indigenous Chinese cattle

2022· dataset· en· W4394542899 on OpenAlexaff
Gaoxiang Yuan, Huixuan Yan, Ying Liu, Xuhong Ding, Xinglei Qi, Kaixing Qu, Fuqiang Li, Jicai Zhang, Suolang Quji, Chuzhao Lei, Bizhi Huang, Lulan Zeng

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA regulation and disease
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntrogressionIndigenousGeographyEthnologyBiologyHistoryGeneticsEcologyGene

Abstract

fetched live from OpenAlex

Historical hybridization between southern indigenous Chinese cattle and banteng has been well-documented and has resulted in gene introgression. Bitter taste receptors were reported in indigenous cattle as a result of introgression from banteng. To determine the level of introgression of the taste 2 receptor member 16 (TAS2R16) gene from banteng into Chinese cattle, two missense mutations in the bovine TAS2R16 gene were examined. Here, we explored the prevalence of the two variants in 28 indigenous Chinese cattle and banteng breeds (comprising 750 individuals) to determine the influence of banteng introgressions on Chinese cattle based on PCR and DNA sequencing. In our study, the two mutant alleles had a higher frequency distribution in southern China with strong geographic distribution, especially in the south-central and southeast areas. In conclusion, this study examines the impact of introgression on the frequency distributions of mutations in variable regions and the subsequent adaptation of Chinese indigenous cattle to different environmental conditions.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.008
GPT teacher head0.263
Teacher spread0.254 · 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 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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