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Record W4308076802 · doi:10.53660/clm-624-688

Avaliação de práticas agropecuárias utilizadas na criação de bovinos carreiros

2022· article· pt· W4308076802 on OpenAlexaff
Maria Clara Oliveira Costa, Carlos Eduardo Emídio Da Silva, Osvaldo José da Silveira Neto

Bibliographic record

VenueConcilium · 2022
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsCollege of Physicians and Surgeons of Ontario
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

A utilização de animais de tração para o auxilio em serviços humanos é uma prática comum das civilizações. O carro de boi é um veículo de tração muito utilizado, e no Brasil, está presente desde a colonização. Objetivou-se com a realização deste trabalho avaliar as práticas agropecuárias utilizadas na criação de bois de carro, ou seja, os bovinos utilizados para a tração dos carros de boi. Foi aplicado um questionário para os produtores responsáveis pela criação dos animais, sendo avaliadas as características relacionadas ao treinamento dos animais, manejo sanitário, nutricional, e fatores que envolvem a relação entre homem, boi e tradição. A raça de maior predileção dos carreiros é o Caracu devido a sua rusticidade, correspondendo a 80% dos diversos utilizados para função de puxar os carros, sendo que a maioria dos animais possuíam entre 1,5 a 2 anos de idade e pelo menos 75% dos produtores vacinaram os animais contra a febre aftosa; 58% para clostridioses e 50% contra a raiva bovina, além de todos os animais receberem vermífugos .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.252
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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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