Bibliographic record
Abstract
This special issue seeks to blend the border between pathology and microbiology, exploring recent developments in the understanding and recognition of infectious diseases of domestic animals. The pathology and pathogenesis of animal infectious diseases is a fascinating world, and veterinary pathologists encounter the breadth of these conditions in the course of their routine activities. Pathogens of domestic animals are important: they lead to suffering and death, are responsible for lost production and performance, underlie the widespread use of antibiotics in veterinary medicine, and cause disease in humans. Pathology is a key tool in the diagnosis, understanding, and control of these diseases. From a more selfish perspective, the lesions and pathogenesis of infectious diseases hold an intrinsic fascination and were a defining motivation for many of us to have focused our careers on veterinary pathology or veterinary infectious diseases. That moment on the microscope when we notice syncytia and intracytoplasmic inclusions in a calf’s lung arouses our natural curiosity of how bovine respiratory syncytial virus incites these lesions and how these lesions incite clinical disease. 16 Seeing is not only believing but also a stimulus for further exploration.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".