Canine tumor development and crude incidence of tumors by breed based on domestic dogs in Gifu prefecture
Bibliographic record
Abstract
We analyzed the status of tumor development in dogs by breed based on tumor cases that presented to the Department of Veterinary Pathology of the Gifu University for diagnostic examinations over eight years (2005-2012). We also calculated the crude incidence of tumors in dogs by breed based on the results of a survey conducted in 2011 in Gifu Prefecture. The most common sites of tumor development included the skin, digestive organs and mammary glands. Smaller dogs showed a tendency to have a higher incidence of breast tumors. We thus identified dog breeds with a higher crude incidence of tumors (Bernese mountain dog, golden retriever, corgi, etc.) and those with a lower crude incidence of tumors (Pomeranian, poodle, Chihuahua, etc.). Unlike the current trends for domestic dogs in the US and Europe, Japan has a higher number of small dogs as pets; it is therefore necessary to develop a policy for canine cancer specific to Japan.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".