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Record W4250689282 · doi:10.36899/japs.2021.4.0287

MORPHOMETRIC CHARACTERIZATION OF LOCAL AND EXOTIC CHICKEN GENOTYPES IN THREE AGRO-ECOLOGIES OF NORTHERN ETHIOPIA

2020· article· en· W4250689282 on OpenAlexaff
Shishay Markos, Berhanu Belay, Tess Astatkie

Bibliographic record

VenueThe Journal of Animal and Plant Sciences · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBiologyGenotypeGeneGenetics

Abstract

fetched live from OpenAlex

The northern part of Ethiopia is endowed with local and exotic breeds of chicken; however, their morphometric characterization needs to be done to support future improvements and conservation. Hence, the study was conducted in three agro-ecologies (lowland, midland, and highland) of Northern Ethiopia to assess the morphometric traits of local and exotic (Naked-neck) chicken genotypes. The experiment was designed as 4x2 factorial with Chicken genotype (4 levels: local from each of lowland, midland and highland, and exotic from lowland) and Sex (2 levels: female and male) being the factors. ANOVA was conducted to determine the main and interaction effects of these factors on 21 morphometric traits. The results revealed significant interaction of Chicken genotype and Sex on all morphometric traits other than skull length, skull width, skull index and neck length highlighting the presence of vast sex specific differences among the genotypes. Apart from comb, earlobe and beak indices and wattle width, male Naked-neck (from lowland) have significantly higher values than the other seven combinations. The morphometric variations of chicken genotypes unveiled in this study are good indicators of genetic diversity of chicken population in Northern Ethiopia and calls for designing community based genetic improvement program to maximize desirable traits. Key words: Highland, Lowland, Midland, Quantitative trait

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score0.146

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 head0.085
GPT teacher head0.215
Teacher spread0.130 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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