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Record W4414972449 · doi:10.1080/09712119.2025.2565417

Boran calves reared under partial milk offtake can undergo compensatory growth post-weaning and optimize beef-milk productivity

2025· article· en· W4414972449 on OpenAlexaff
Jack Ouda, D. Indetie, George Karumba, Robert Irungu

Bibliographic record

VenueJournal of Applied Animal Research · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsResponse Biomedical (Canada)
FundersGovernment of the Republic of Kenya
KeywordsMilkingCompensatory growth (organ)WeaningIce calvingMatingBody weightProductivity

Abstract

fetched live from OpenAlex

This study determined the long-term effect of restricted suckling on the post-weaning performance of Boran cattle. Thirty-two nursing cows and their calves arranged in a 4 X 2 factorial experiment were grazed in the tropical ecological zone IV range. The treatments were Full Suckling (FS), Milking from 1 Quarter (M1Q), milking from 2 Quarters (M2Q) and milking from 3 Quarters (M3Q) of the udder. Each treatment had eight calves, four of each sex. Weaning was at 210 days of age. At weaning, the M3Q calves had the lowest weight (P < 0.01), while the other treatments had similar weights. The highest (P < 0.01) post-weaning weight gains were during 18–24 months of age by M3Q treatment, which were 382 and 373 g/day for females (heifers) and males (steers), respectively. Steers from all treatments had similar weights from 12 months onwards while all the heifers reached the minimum mating weight set at 270 kg by 31 months. Their mating and calving were successful. The findings revealed that Boran calves can survive to weaning when suckling only a quarter (25%) of milk produced by dams yielding at least four litres daily. Upon weaning, compensatory growth occurs and enables catching up by 12–24 months of age.

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 score0.932
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.312
Teacher spread0.257 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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