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Record W2605272934 · doi:10.6000/1927-5129.2017.13.10

Analysis of Growth and Carcass Traits of Dumbi Sheep Breed Male Lambs Different Management Systems

2017· article· en· W2605272934 on OpenAlexvenueno aff
Muhammad Akram Safi, Huma Rizwana, Hubdar Ali Kaleri, Asma Kaleri, Kamal-Uddin Mandokhial, Abdul Satar Safi, Rameez Raja Kaleri, Asad Ullah, Muhammad Rasheed

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal husbandryBreedFodderLivestockGrazingBiologyAnimal scienceCarcass weightBody weightVeterinary medicineAgricultureMedicineAgronomyEcology

Abstract

fetched live from OpenAlex

Present research was performed on twelve male lambs of Dumbi sheep breed kept in 2 management systems at Faculty of Animal Husbandry and Veterinary Sciences, | department of Livestock Management, Sindh Agriculture University, Tandojam. Animals were divided into two different groups. A groups animal were kept in semi intensive with provision of open grazing and concentrates while, Bgroup animals were kept in intensive management system with provision of green fodder and concentrate. Study was performed till 8 weeks and lambs were observed weekly foraverage body weight and carcass characteristics of both groups were recorded. The results of current study showed that average body weight of group A was (8.33 kg)found significantly high (P>0.05) as compared to the group B (6.86 kg). Moreover carcass characteristics of Dumbi lamb was also observed higher in group A as compared group B. While during comparison of the economical values of both groups, it was observed that group A was found more economical than group B. It is concluded that semi-intensive management system was found better as compared to intensive management system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.306

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 science0.0000.000
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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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