Surveillance, Encompassing Public Health to Your Animal Companion
Bibliographic record
Abstract
Overseas, the surveillance on companion animals have been reported by "NASPHV (National Association of State Public Health Veterinarians)", "CCWHC (Canadian Cooperative Wildlife Health Centre)", "ECDC-EFSA (European Center for Disease Prevention and Control - European Food Safety Authority)", "CSVID (Chilean Society of Veterinary Infectious Diseases)". According to pet boom in Japan, the disease surveillance of pets utilized IT (information technology) becomes available by spreading of "insurance-for-pets" and Electric Health Record in animal hospitals. We report challenges and results of new pet-surveillance by using of clinical data in animal hospitals. Clinical data of companion animals were analyzed by using cloud computing and electrical patient records without personal identifiers. Analyzed history, profile and clinical information of cases were rapidly feedback to veterinary surgeons in automated fashion from the system. The surveillance is designed with intention to answer some questions of the transmission routes of zoonotic diseases. Cloud computing combined with electrical patient records provided environment for easy daily operation with automated real-time feedback of data collation and limited descriptive analysis as additional advantage. Public and animal health epidemiologist may be able to provide further time trend, statistical, and geo-spatial analysis using with whole or subsets of this readily available database. Together the surveillance will improve understanding of pet animal health in the community and transmission of zoonosis in between human and animal.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".