Avaliação da condição corpórea em cães utilizando o indice de massa corpórea (IMC) e escore de condição corpórea (ECC)
Bibliographic record
Abstract
The maintenance of ideal dog weight may prevent many diseases. This study goal to evaluate the body condition in Labrador Retriever dogs using the body mass index and body score. Data were collected from dogs using the methodology reported in the literature. 30 animals evaluated, 11 were females and 19 were males; with a mean age 3.6. The weight of the animals ranged from 23.0 to 54.0 kg with a mean value of 33.7 kg, 34.1 kg for males and 35.1 kg for females. Only 8 dogs, six males and two females were castrated. After data analyzing, 30 animals were evaluated and divided into four categories, according to the ECC 23.3% (7/30) dogs were considered to be obese, 43.3% (13/30) fatty, 23, 3% (7/30) ideal and 10% (3/30) of lean animals. According to IMCC evaluation averaged 15.5 with a range from 11.8 to 21.1. According to IMCC reference values, one of the animals had to underweight, 14 dogs were presented with the ideal weight, 14 overweight and only one obese. Condition body evaluation by IMCC technique is easy to perform and interpret. However, the comparison between both methods ECC and IMCC presented different classification in 46.7% of the animals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".