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Record W3154758984

Equations used to develop commercial dog food feeding guidelines and Canadian owner feeding practices in 2018.

2021· article· en· W3154758984 on OpenAlexaffabout
Katja A. Sutherland, Cara Cargo-Froom, Adronie Verbrugghe, Anna K. Shoveller

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBusinessEnergy densityPet foodCompliance (psychology)Energy requirementPsychologyMarketingFood scienceEngineeringBiologySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Feeding guidelines on commercial dog food packages provide a suggested starting point for food provision for dogs. The equations used to develop commercial dog food feeding guidelines and the owner feeding practices surveyed were examined in this study. The equations used to develop feeding guidelines and the energy density calculation method (Traditional Atwater or Modified Atwater equation) were investigated for 200 dry dog foods sold in Canada. Not all energy densities of products were calculated using the modified Atwater equation, a requirement if claiming compliance with the Association of American Feed Control Officials (AAFCO). Commercial feeding guidelines provide conservative estimates of pet dog energy needs. A survey of dog owners' feeding practices was conducted, with 739 responses analyzed. Respondents generally took appropriate action to manage the weight of their dogs through both exercise and dietary management. Further investigation should explore how owners may be successfully managing their dogs' weight without veterinary supervision.

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)0.005
metaresearch head score (Gemma)0.017
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.401
GPT teacher head0.384
Teacher spread0.016 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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