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Comparative Meat Production Performance Evaluation of Buffalo with Cattle at Different Ages

2017· article· en· W2783121210 on OpenAlexvenueno aff
Biplob Kumer Roy, Khan Shahidul Haque, Nazmul Huda

Bibliographic record

VenueJournal of Buffalo Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsProduction (economics)Animal productionAnimal scienceBiologyBiotechnologyEconomics

Abstract

fetched live from OpenAlex

An inquisitive on-station feeding trial was carried out to identify the dexterous species and age for beef production with same plane of nutrition. A 2×3 (2 species × 3 ages) factorial experiment was settled for a period of 105 days with eighteen native buffalo and 18 BCB-1 (BLRI Cattle Breed-1) bulls of three age groups (18 months, 24 months and 30 months) and distributed them randomly in six treatment groups having an equal number (6) of animals in each. Intake of nutrients i.e.: DM, CP of buffalo bulls was significantly (p<0.001) higher than BCB-1 bulls in all the cases. The buffalo bulls had significantly higher digestibility of DM (68.0%, p<0.001), OM (67.9%, p<0.001), CP (66.3%, p<0.05), ADF (59.8%, p<0.001) or NDF (59.6%, p<0.001) than cattle (63.0%, 62.7%, 63.6%, 52.4% & 49.6%, respectively). But, the digestibility of DM, OM, CP, ADF or NDF was not affected significantly (p>0.05) by the age of the bulls with any cases. Buffalo bulls gained body weight more rapidly (p<0.001); 1.11 & 0.88 kg/day, respectively and showed a better FCR (p>0.05; 6.72 & 6.86, respectively) than cattle with low feed cost of per kg gain (US$ 1.62 & US$ 1.69, respectively). ADG (p<0.01), FCR (p<0.05) and estimated feed cost (p<0.05) affected significantly and increased linearly by the age of bulls, where 18 months bulls of buffalo and BCB-1 performed best. In an aggregation, it revealed that, buffalo performed better than BCB-1 cattle and 18 months age of both species was more responsive for profitable meat production.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.480

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 studies0.0010.001
Scholarly communication0.0000.001
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 head0.098
GPT teacher head0.315
Teacher spread0.217 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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