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Record W3208405027 · doi:10.5539/sar.v10n4p33

Factors Affecting Weight Gain in Nelore Calves from Birth to Weaning in the Bolivian Tropic

2021· article· en· W3208405027 on OpenAlexvenueno aff
Atsuko Ikeda, Montellano-Paco Arturo, Ivana Barbona, Marini Roberto Pablo

Bibliographic record

VenueSustainable Agriculture Research · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural and Food Production Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWeaningIce calvingAnimal scienceBirth weightBody weightBiologyWeight gainPregnancyLactationEndocrinology

Abstract

fetched live from OpenAlex

Retrospective data corresponding to the period between 2002 and 2018 were used, belonging to the Cooperativa Agropecuaria Integral San Juan de Yapacaní, Santa Cruz, Bolivia. Data corresponding to 663 male and female calves born to primiparous and multiparous cows were used. The calves had 13 individual weight controls. The calves had 13 individual weight controls. Variables used: Date of birth, Calf live weight at birth in kg, Calf live weight at weaning in kg, Average daily gain in kg, Live weight of cow in kg, Number of calving of cows. The mean values and standard deviations of the weights at birth were 35.1 ± 4.6 kg for males and 32.3 ± 4.7 kg for females, at 240 days (weaning) the weight of the males was 229 ± 35.8 kg and for females 206 ± 31.5 kg. The mean values and standard deviations of the weight increases were 0.807 ± 0.14 kg for males and 1.0 ± 0.13 kg for females. The selected model with the regressor variables: Year, Sex of the calf, Number of calving and Live Weight at Birth, all significant (p ≤0.001). No interaction was significant to be considered in the model (p≥0.05). The live weight at birth, the sex, the years and the number of deliveries of the mothers showed in this work and for the animals analyzed to be the factors affecting the increase in live weight in the rearing stage (from birth to weaning) in Nelore calves in the Bolivian tropics.

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)0.001
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 score0.322
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.062
GPT teacher head0.304
Teacher spread0.242 · 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
Published2021
Admission routes1
Has abstractyes

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