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Record W4313387710 · doi:10.56093/ijans.v88i5.79972

Identification of quantitative trait loci for milk yield in Murrah buffaloes

2018· article· en· W4313387710 on OpenAlexaff
Upasna Sharma, Priyanka Banerjee, Jyoti Joshi, Prerna Kapoor, Ramesh Kumar Vijh

Bibliographic record

VenueThe Indian Journal of Animal Sciences · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsDalhousie University
FundersIndian Council of Agricultural Research
KeywordsSireBiologyQuantitative trait locusMicrosatelliteGeneticsMurrah buffaloChromosomeAnimal scienceAlleleGene

Abstract

fetched live from OpenAlex

A reference family consisting of 12 half sib sire families were created for the identification of QTLs for milk yield in buffaloes. Daughters were recorded for monthly test day milk yield. The number of daughters per sire varied from 50 to 335 daughters per sire. Seventy nine polymorphic microsatellite markers located on 8 chromosomes were genotyped for 2281 daughters of the 12 sires. Whole chromosome scanning was done using single marker analysis and interval mapping using three different algorithms. The analysis was carried out sire family wise. QTLs (63) were identified in single marker analysis and 32 QTLs were identified using interval mapping. The significance of LOD score was tested using permutation tests. The metaQTL analysis was carried out to find out the consensus chromosomal regions associated with milk yield in buffaloes. Five models were utilised and the best was selected on the basis of Akaike Information content. Total 23 chromosomal regions were identified for milk yield in buffaloes. 2 metaQTL chromosomal regions were identified on buffalo chromosome BBU2q; 3 metaQTLs each on buffalo chromosomes BBU8, BBU10 and BBU15 and 4 metaQTL regions each on BBU1q, BBU6, BBU9.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.130

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.043
GPT teacher head0.302
Teacher spread0.259 · 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 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

Citations4
Published2018
Admission routes1
Has abstractyes

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