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Record W2547059412 · doi:10.5380/avs.v21i1.43628

CONSUMO VOLUNTÁRIO DE ENERGIA POR CÃES DE DIFERENTES RAÇAS

2016· article· pt· W2547059412 on OpenAlexaboutno aff
Larissa Wünsche Risolia, Simone Gisele de Oliveira, Ananda Portella Félix, Cleusa Bernardete Marcon de Brito, Alex Maiorka

Bibliographic record

VenueArchives of Veterinary Science · 2016
Typearticle
Languagept
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsBeagleAnimal sciencePhysicsBiologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

O presente estudo teve como objetivo avaliar o consumo voluntário de energia por cães de diferentes raças. Para a realização do experimento, utilizou-se 16 cães adultos, machos e fêmeas, de raças distintas, alimentados com uma ração completa seca extrusada para cães em manutenção, uma vez ao dia durante 30 minutos. A quantidade fornecida foi 30% superior às necessidades de energia metabolizável de mantença (NEM). O delineamento foi inteiramente ao acaso e os resultados foram submetidos à análise de variância, sendo as médias comparadas pelo teste Tukey a 5% de probabilidade. Os cães da raça Labrador apresentaram consumo de 20% acima da energia recomendada, enquanto que os Huskies consumiram 26% menos energia que o recomendado para a raça. Em contrapartida os cães da raça Beagle apresentaram seu consumo de energia mais próximo ao recomendado pelo NRC. Diante dos resultados obtidos, conclui-se que cães da raça Labrador apresentaram maior consumo de energia dentre as raças avaliadas, seguidos por Basset hound, Beagle e Huskie siberiano.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.837

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.019
GPT teacher head0.278
Teacher spread0.260 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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