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Record W4414831435 · doi:10.1093/jas/skaf300.127

156 Rationale for different diets for dogs based on their size.

2025· article· en· W4414831435 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Animal Science · 2025
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
Fundersnot available
KeywordsBreedFecesBody weightEnergy metabolismEnergy densityLabrador RetrieverAdipose tissue

Abstract

fetched live from OpenAlex

Abstract American Kennel Club (AKC) has recognized 201 pure breeds. These adult dogs weigh as little as 2 lbs. and over 250 lbs. and in between. At the same time mixed breed dogs also fall in the weight range. The canine nutritionists are aware that when the same diet fed to the small and large dog, the fecal consistency of the larger dogs will be softer. The physical features of small and large dogs are obvious. However, research has revealed gastrointestinal differences that could lead to the softer stool in the large sized dogs as compared to smaller sized dogs. While formulating food for dogs of different size, considerations should be given to: (1) Kibble size and texture based on jaw structure of the dogs. (2) Energy content in the food. Energy content in food for smaller dogs generally contains higher energy because of the higher proportion of active metabolic tissues per unit of the body as compared to larger dogs. (3) The body composition. Energy content in the food for the dogs weighing the same will vary based on their body composition. For example, Boxers and Labrador Retriever (Lab) weighing 60 lbs., energy content in the Boxer diet will be higher than Lab because Boxers are muscular as compared to Labs who contain higher proportion of adipose tissues. (4) Differences in gastrointestinal (GI) physiology of the small & large dogs. Most of the discussion in this presentation will be based on this difference. Weber et.al. (2002) confirmed the assumption that when the same diet was fed to four groups of dogs of varying sizes and body weight, the proportion of water increased in the feces as the dog size increased. The natural assumption was that watery stools in large sized dogs could be the result of poor food digestibility. However, the results confirmed that the apparent nutrient digestibility of major nutrients in larger and giant dogs was higher than small and medium sized dogs. There was also no significant difference for nutrient absorption amongst dogs of various sizes. Therefore, the assumption of poor nutrient digestibility and poor nutrient absorption in larger dogs resulting into poor stool consistency was rejected. The transit time of food from stomach to large intestine was similar across the size of the dogs. However, colonic transit time (CTT) significantly differed across the size of the dogs. CTT in small dog was 9.1-hours, medium sized dog was 18.5 hours, large sized dog was 39.4 hours and giant sized dog was 29.3 hours. Therefore, the average length of time food stayed in the colon was 39% in small dogs and 70% in large dogs. A strong relationship was noticed in the increased CTT and poor fecal score in the dogs. The longer CTT results in the increased bacterial fermentation leading to an increased production of the short chain fatty acids (SCFA) and lactic acid. SCFA have affinity towards water and thus hold on to it. As not all SCFA and lactic acid got absorbed, therefore, their excretion along with water in feces also increases. The net result is an increased proportion of water in the feces of large dogs. Moreover, sodium digestibility is lower in larger dogs as compared to smaller dogs. Because of the relationship between sodium and water, the water in the feces also increase. Therefore, all the morphological and physiological differences in dogs must be considered while formulating diets for dogs of different size. References: Weber, M.; Martin, L.; Biourge, V.; Nguyen, P., 2002: AJVR 63: 1323.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0130.006

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 head0.033
GPT teacher head0.330
Teacher spread0.297 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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