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Record W7132879964

A canine model for assessment of satiety

2005· dissertation· W7132879964 on OpenAlexfundno aff
Wai Chung Joan Chan

Bibliographic record

VenueTSpace · 2005
Typedissertation
Language
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
FundersUniversity of TorontoHill's Pet Nutrition
KeywordsTask (project management)Food intakeAppetiteAlternation (linguistics)Protocol (science)MealBeagleObesity
DOInot available

Abstract

fetched live from OpenAlex

Overweight and obesity are commonly treated with weight-loss agents that reduce food intake through alternation to the parameters that influence meal ingestion. The mechanism of action, efficacy, and toxicological profiles of these agents are firstly assessed in animals and finally in humans. However, the currently available animal models and methods of assessment are not sufficient and sensitive enough to detect the minute changes in appetite and feeding motivation prior to the observable and quantifiable reduction in food ingestion. Hence, the current study attempts to develop and promote a novel cognitive-based protocol to assess satiety in dogs. The visuospatial memory task used in this protocol was also used to evaluate the satiability of a high-fibre diet and the anorexigenic effect of d-amphetamine. All three experiments demonstrated that the visuospatial memory task was a sensitive and valid method in assessing feeding motivation and comparing the efficacy of potential weight-reducing agents in beagle dogs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.148
GPT teacher head0.466
Teacher spread0.318 · 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.

Study designSimulation or modeling
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
Published2005
Admission routes1
Has abstractyes

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