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Record W2609873869 · doi:10.1111/jpn.12709

Estimation of maintenance energy requirements in German shepherd and Labrador retriever dogs in Bangalore, India

2017· article· en· W2609873869 on OpenAlexaboutno aff
H. S. Madhusudhan, K. Chandrapal Singh, U. Krishnamoorthy, Kumar Garg Umesh, Richard F. Butterwick, David J. Wrigglesworth

Bibliographic record

VenueJournal of Animal Physiology and Animal Nutrition · 2017
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsLabrador RetrieverGerman Shepherd DogAnimal scienceBody weightEnergy requirementFood intakeEnergy expenditureMedicineBiologyVeterinary medicineInternal medicineSurgeryMathematicsStatistics

Abstract

fetched live from OpenAlex

Summary Maintenance energy requirements ( MER s) were calculated for 17 German shepherd and 20 Labrador retriever adult dogs using an in‐home prospective dietary trial. The dogs were fed the same dry pet food and body weight, food intake, body condition score and physical activity were monitored for 10 weeks. Labrador retrievers were significantly heavier and had higher body condition scores than German shepherd dogs, but there was no difference between males and females within each group. Body weights remained stable over the study period, with an average daily gain of 9.1 g. Mean ( SD ) MER was 103.4 (16.3) kcal/kg BW 0.75 , which was some 20% lower than that currently suggested for moderately active young adult dogs. Individual MER ranged from 66.8 to 141 kcal/kg BW 0.75 . There were no significant differences in MER between the two breeds, or between males and females within and between the two breeds. There was a significant inverse relationship between MER and body condition score, reflecting the lower energy expenditure of adipose tissue. The lower MER of dogs in this study, relative to previous observations, may reflect climatic and environmental differences and highlight the necessity for accurate estimates of MER in relation to the production and feeding of pet foods.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.424

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 communication0.0000.001
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 head0.042
GPT teacher head0.333
Teacher spread0.291 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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