Exotic and Emerging Diseases of Animals: An Internet Course for Veterinary Students
Bibliographic record
Abstract
US agricultural and companion animals are very vulnerable to the introduction of exotic and emerging animal diseases (EEAD). These diseases could occur through unintentional introduction (the risk of outbreaks grows as free trade increases), could occur through the deliberate introduction of disease agents (bio-terrorism or agro-terrorism), or could emerge as new diseases. EEAD, for the purpose of this course, are defined as those animal diseases that are reportable in the US. This includes diseases on the Office international des épizooties (OIE) List A, selected diseases on List B that either are not found in the US or are reportable, and selected emerging diseases. Some of the exotic and emerging diseases are considered to be foreign animal diseases because they do not occur in the US. Others are found in the US but are under eradication programs. Some are zoonotic and must be monitored and controlled to protect human health. Many of these diseases are important causes of animal suffering and are economically very important. It is essential that veterinarians be familiar with these diseases and have access to accurate, concise information about their salient characteristics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.200 | 0.104 |
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".