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Record W1852413816 · doi:10.1017/s175173111500097x

Among farm variation in heifer BW gains

2015· article· en· W1852413816 on OpenAlexafffundabout
Guilherme Borges Bond, M.A.G. von Keyserlingk, N. Chapinal, Edmond A. Pajor, Daniel M. Weary

Bibliographic record

Venueanimal · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversity of CalgaryUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsAnimal scienceWeaningBiologyVeterinary medicineMedicine

Abstract

fetched live from OpenAlex

BW of replacement heifers is rarely measured on commercial farms, making it difficult to evaluate the success of management practices related to calf growth. Our aims were to describe variability among commercial farms in Holstein heifer BW, determine how BW differences varied with management and propose a method of estimating calf growth based upon single measurement. Heart girth circumference was used to estimate BW of 576 heifers 48 to 70 weeks of age on 33 different farms (on average 11 ± 6 heifers/farm) in British Columbia, Canada. Regression analysis showed a linear relationship of BW with age (BW (kg)=116+5 × age (weeks)). Residuals from this regression were averaged across heifers within each farm to identify farms where heifers were heavier or lighter than would be predicted on the basis of their age; farm average residuals ranged from -54 to 72 kg. Farms with heifers showing the highest residual BW also had the highest rates of gain for pre-weaned calves. These results indicate that farms able to rear faster growing calves before weaning were also rearing faster growing heifers at breeding, and suggest that management of milk-fed calves is a particularly important component of replacement heifer management.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.293

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 head0.024
GPT teacher head0.261
Teacher spread0.237 · 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

Citations9
Published2015
Admission routes3
Has abstractyes

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