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Record W3177543107 · doi:10.1111/age.13119

Development of a 19‐plex short tandem repeat typing system for individual identification and parentage testing of horses (<i>Equus caballus</i>)

2021· article· en· W3177543107 on OpenAlexaff
Songyang Shang, Ruolan Jiang, Rongjian Luo, Suqi Jia, David M. Irwin, Z. Wang, Shufan Zhang

Bibliographic record

VenueAnimal Genetics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of Toronto
FundersDepartment of Education of Liaoning Province
KeywordsBiologyGeneticsLocus (genetics)EquusMicrosatelliteTypingSTR multiplex systemMultiplexPopulationTandem repeatIdentification (biology)Evolutionary biologyComputational biologyAlleleZoologyGeneGenome

Abstract

fetched live from OpenAlex

Individual identification of horses for pedigree verification and registration is important for the sustainable development of the horse industry. Horse individual identification and parentage tests commonly use the 17 short tandem repeats (STRs) recommended by the International Society for Animal Genetics (ISAG) and the locus LEX33. While many multiplex STR typing systems have been established for the horse, a sex determining marker is usually absent, and none of them can simultaneously detect all 17 ISAG recommended loci and the locus LEX33. Here, we present a 19-plex STR typing system that contains the 17 ISAG recommend loci, the locus LEX33 and amelogenin as sex determining loci. The results of our sensitivity, species specificity, stutter analysis and population data analysis, indicate that this system is a specific, sensitive, and robust tool for the identification of individuals, parentage testing and genetic research in the horse.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.053
GPT teacher head0.276
Teacher spread0.223 · 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 designBench or experimental
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

Citations5
Published2021
Admission routes1
Has abstractyes

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