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Record W4411465174 · doi:10.5194/aab-68-409-2025

Genetic diversity and population structure of the indigenous goat population in Tunisia's northwest

2025· article· en· W4411465174 on OpenAlexaff
Ikram Bensouf, Ines Dhib, Safa Bejaoui, Hatem Ouled Ahmed, Hichem Khemiri, Naceur M’Hamdi

Bibliographic record

VenueArchives animal breeding/Archiv für Tierzucht · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
Fundersnot available
KeywordsGenetic diversityInbreedingBiologyPopulationAnalysis of molecular varianceGenetic variationMicrosatelliteGenetic variabilityAlleleGenetic structureGenetic distanceEvolutionary biologyGeneticsVeterinary medicineDemographyGenotypeGene

Abstract

fetched live from OpenAlex

Abstract. Elucidating genetic diversity is critical for improving breeding strategies. To this end, the present study investigated the genetic diversity and population structure of the local goat population sampled from the northwestern region of Tunisia. The genetic variability was estimated using seven microsatellites, which revealed high diversity and genetic population clustering with a dispersed geographical distribution. All of the markers showed a significant genetic polymorphism, with an average of 18 alleles. Thus, 123 alleles were detected in the seven loci of the six studied populations. This result reflects the existence of a significant polymorphism within the local goat population. Overall, the highest HO was 0.952, and the highest HE was 0.942. Furthermore, the goat population demonstrated negative FIS (−0.177). This negative value shows an overall excess of heterozygotes, suggesting the absence of inbreeding. Analysis of molecular variance (AMOVA) revealed that genetic differences within the population explained 92.4 % of the variation. A low average FST (0.076) suggests intermixing among Tunisian goats. Genetic distance values range from 0.319 to 0.985.

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 score0.046
Threshold uncertainty score0.647

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 science0.0000.001
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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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