Bibliographic record
Abstract
Veterinary forensic pathology is emerging as a distinct discipline, and this special issue is a major step forward in establishing the scientific basis of the discipline. A forensic necropsy uses the same skill set needed for investigations of natural disease, but the analytical framework and purpose of forensic pathology differ significantly. The requirement of legal credibility and all that it entails distinguishes the forensic from routine diagnostic cases. Despite the extraordinary depth and breadth of knowledge afforded by their training, almost 75% of veterinary pathologists report that their training has not adequately prepared them to handle forensic cases. Many veterinary pathologists, however, are interested and willing to develop expertise in the discipline. Lessons learned from tragic examples of wrongful convictions in medical forensic pathology indicate that a solid foundation for the evolving discipline of veterinary forensic pathology requires a commitment to education, training, and certification. The overarching theme of this issue is that the forensic necropsy is just one aspect in the investigation of a case of suspected animal abuse or neglect. As veterinary pathologists, we must be aware of the roles filled by other veterinary forensic experts involved in these cases and how our findings are an integral part of an investigation. We hope that the outcome of this special issue of the journal is that veterinary pathologists begin to familiarize themselves with not only forensic pathology but also all aspects of veterinary forensic science.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".