Feline coronavirus and feline infectious peritonitis (FIP) – Russian roulette for your pet
Bibliographic record
Abstract
Feline coronavirus and feline infectious peritonitis (FIP) – Russian roulette for your pet Utilising Machine Learning on clinical datasets could help to crack the enigma of feline infectious peritonitis diagnosis. Coronaviruses came to the forefront of public consciousness in 2019 with the outbreak of the SARS-CoV-2 pandemic. However, this family of viruses has long been recognised as important pathogens of animals and man. Feline coronavirus (FCoV) is a ubiquitous pathogen of cats, which can sometimes cause a devastating disease called ‘feline infectious peritonitis’ (FIP) in both domestic and wild felids. This virus is common among pet cats and in multi-cat households and shelters, where its prevalence can be extremely high. Infection is reasonably innocuous for most cats, who may experience asymptomatic infection or develop a mild gastrointestinal upset. However, similar to COVID-19 in humans, sometimes infection has more severe consequences. In a small fraction of cases, usually between 5 and 10% of FCoV-infected individuals, (1) cats develop a severe aberrant immune response to the virus, resulting in FIP. Different types of FIP occur, affecting different tissues, and until very recently, the disease was invariably fatal.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.016 |
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".