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Record W2100192967 · doi:10.1111/age.12324

Mitochondrial <scp>DNA</scp> variation of <scp>N</scp>igerian domestic helmeted guinea fowl

2015· article· en· W2100192967 on OpenAlexaff
Adeniyi C. Adeola, Sheila C. Ommeh, Robert W. Murphy, Shi‐Fang Wu, Min‐Sheng Peng, Ya‐Ping Zhang

Bibliographic record

VenueAnimal Genetics · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLivestock and Poultry Management
Canadian institutionsRoyal Ontario Museum
Fundersnot available
KeywordsBiologyMitochondrial DNAGuinea fowlFowlGuinea pigMolecular biologyGeneticsGeneVeterinary medicine

Abstract

fetched live from OpenAlex

We analyzed genetic diversity of 215 mitochondrial DNA (mtDNA) D-loop sequences from seven populations of domesticated helmeted guinea fowl (Numida meleagris) in Nigeria and compared that with results of samples collected in Kenya (n = 4) and China (n = 22). In total, 241 sequences were assigned to 22 distinct haplotypes. Haplotype diversity in Nigeria was 0.693 ± 0.022. The network grouped most matrilines into two main haplogroups: A and B. There was an absence of a geographic signal, and two haplotypes dominated across all locations with the exception of the Kebbi population in the northwest of the country; AMOVA also confirmed this observation (FST = 0.035). The low genetic diversity may be a result of recent domestication, whereas the lack of maternal genetic structure likely suggests the extensive genetic intermixing within the country. Additionally, the differentiation of the Kebbi population may be due to a certain demographic history and/or artificial selection that shaped its haplotype profile. The current data do not permit us to make further conclusions; therefore, more research evidence from genetics and archaeology is still required.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.030
GPT teacher head0.238
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 source (direct Gemma or distilled Codex), 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

Citations12
Published2015
Admission routes1
Has abstractyes

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