Bibliographic record
Abstract
The rise of forensic pathology in human medicine has greatly contributed to the administration of justice, public safety and security, and medical knowledge. However, the evolution of human forensic pathology has been challenging. Veterinary forensic pathologists can learn from some of the lessons that have informed the growth and development of human forensic pathology. Three main observations have emerged in the past decade. First, wrongful convictions tell us to use a truth-seeking stance rather than an a priori "think dirty" stance when investigating obscure death. Second, missed homicides and concealed homicides tell us that training and certification are the beginning of reliable forensic pathology. Third, failure of a sustainable institutional arrangement that fosters a combination of service, research, and teaching will lead to stagnation of knowledge. Forensic pathology of humans and animals will flourish, help protect society, and support justice if we embrace a modern biomedical scientific model for our practice. We must build training programs, contribute to the published literature, and forge strong collaborative institutions.
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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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".