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Probability density function of the number of embryos collected from superovulated Nelore breed donors

2009· article· en· W2110266028 on OpenAlexfundno aff
Renato Travassos Beltrame, L. G. Barioni, Célia Raquel Quirino, Ozanival Dario Dantas

Bibliographic record

VenueScientia Agricola · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le CancerConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsBreedZebuHerdStatisticsEmbryo transferProbability density functionAnimal scienceBiologyEmbryoSelection (genetic algorithm)MathematicsExponential functionGenetics

Abstract

fetched live from OpenAlex

Several models have been developed to evaluate reproductive status of cows through concentration of progesterone in milk, the effect of sex selection in the commercial production of herds and bioeconomic performance of the multiple ovulation and embryo transfer system in select herds. However, models describing the production of embryos in superovulated females have yet to be developed. A probability density function of the number of embryos collected by donors of the Nelore breed was determined. Records of 61,928 embryo collections from 26,767 donors from 1991 to 2005 were analyzed. Data were provided by the Brazilian Association of Creators of Zebu and Controlmax Consultoria e Sistemas Ltda. The probability density function of the number of viable embryos was modeled using exponential and gamma distributions. Parameter fitting was carried out for maximum likelihood using a non-linear gradient method. Both distributions presented similar level of precision: root mean square error (RMSE) = 0.0072 and 0.0071 for the exponential and gamma distributions, respectively; both distributions are thus deemed suitable for representing the probability density function of embryo production by Nelore females.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.314

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 head0.009
GPT teacher head0.205
Teacher spread0.197 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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