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Record W7151681387

Šunų nutukimo analizė Kipro Pafos X smulkiųjų gyvūnų klinikoje

2019· dissertation· en· W7151681387 on OpenAlexaboutno aff
Nikolas Antoniou

Bibliographic record

VenueLithuanian University of Health Sciences · 2019
Typedissertation
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsNeuteringObesityBreedCrossbreedOverweightAnimal healthCompanion animal
DOInot available

Abstract

fetched live from OpenAlex

Research’s aim: To find out which are the main risk factors predisposing dog in obesity development as well as the investigation of the prevalence of dog obesity in X small animal clinic. The research was carried out in X clinic in Cyprus over a period of 4 months. The survey included 26 multiple choice questions and a single open question. 249 dogs took part in the survey including pure breed dogs as well as crossbred ones. A BCS scale (five point system) was attached to each questionnaire in order to assist the owners of the dogs in body condition scoring of their dogs. As well as dog owners were asked to answer questions regarding dogs age, gender, neutering status, diet, activity level as well as concurrent diseases related to obesity if presented and eventually owners of the dogs were questioned whether they are informed about treatment and preventive measures against obesity in dog. Eventually obese dogs were isolated from the rest of the sample and data was analyzed. Statistical calculations showed that older dogs are more prone to obesity, 75 perc. of obese dogs are older than 5 years old. Most obese dogs belong to these breeds: Beagles, Labrador Retrievers, Pekingese, Dachshund, as well as crossbreed dogs. Obesity is related to dog owner’s attitude while 69,83 perc. of dog owners who own obese dogs aren’t aware of their dog’s weight. Furthermore 62,50 perc. of owners who keep more than one dog in the household indicate a lack of attention to each of their dog’s health and weight status as well as 57,45 perc. of owners are not aware of preventive measures for obesity. Dogs whose owners provide them dry food once a day or constantly, do not estimate their feed amount, giving table scraps and treats are more likely to be obese. Limited mobility within their permanent living environment, limited physical activity- not walking at all or walking once or twice a day till 15 min. predispose dogs to obesity development. Obese dogs are more likely to suffer from osteoarticular disorders, heart problems, respiratory problems or skin disease. Obese dogs owners do not have sufficient knowledge regarding treatment of obesity. The main preventive measures are daily exercise and providing the dog with correct food amount.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.002

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 head0.096
GPT teacher head0.357
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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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