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Record W4281655952 · doi:10.5539/ijb.v14n1p19

Weight at First Calving and Its Relationship With Productive Indicators in Nelore Cows in a Grazing System of the Bolivian Tropics

2022· article· en· W4281655952 on OpenAlexvenueno aff
Atsuko. Ikeda, Pablo Roberto Marini

Bibliographic record

VenueInternational Journal of Biology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural and Food Production Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIce calvingGrazingTropicsAnimal scienceCow-calfBiologyWeight gainBody weightHerdLactationAgronomyPregnancyEcologyEndocrinology

Abstract

fetched live from OpenAlex

Considering only selection for increased weight gain until after one year could cause adult cow weight gain that would not be desirable depending on the production system.  To evaluate the relationship of weight at first calving and its relationship with productive indicators in Nelore cows in a grazing system of the Bolivian tropics. Retrospective data from the years 1992 to 2019 were used, which were part of two cooperatives: Agropecuaria Integral San Juan de Yapacaní and the Centro Tecnológico Agropecuario located in Santa Cruz de la Sierra, Bolivia. The data corresponding to 1052 Nelore primiparous cows were used for the research work. The lightest cows had the same calf weight at birth as the rest of the heaviest cows, weaned a lighter calf, arrived the first calving rapidly, showed no differences with the Accumulated Production and the Calf Index with the heaviest cows, but had the highest stock efficiency. Identifying the group of lightest cows as the most efficient was considered a positive tool to recommend producers to take into account at the time of selection. The study shows that the age at first calving is related to indicators of productive efficiency in Nelore cows in a grazing system of the Bolivian tropics and that their use would have a greater impact in identifying the most efficient cow for each production 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.106

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 head0.018
GPT teacher head0.219
Teacher spread0.201 · 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
Published2022
Admission routes1
Has abstractyes

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