Bibliographic record
Abstract
Death Investigation from an Historical Perspective Death Investigation from an Internations Perspective Death Investigation: Operational Rules Deaths and Other Reported Incidents Death Investigation Powers of the Coroner Death Scene Investigation Specialist Death Scenes and Investigations International Disaster Management: Mass Fatalities The Role of the Forensic Pathologist The Autopsy: Medical Issues Autopsies: Legal and Cultural Issues Identification of Human Remains Specialist Medical and Scientific Investigations The Interpretation of Injuries and Medical Findings The Medical Report and the Giving of Evidence Advocacy Inquest Hearings Inquest Findings, Recommendations and Reports Appeals, Reviews and Reopening of Inquests Death Investigation and Coroners: The Future Appendix 1 Coronial Death Investigation: Operational Activities Appendix 2 Examples of coroners' findings and recommendations Appendix 3 A coroner's information booklet Appendix 4 The Australian National Coroners Information System Appendix 5 Medical report and pro forma checklists Appendix 6 Body Charts Appendix 7 Police death notification form for the coroner Appendix 8 Quebec Code of Ethics for Coroners Appendix 9 Practice direction: Guidelines for expert witnesses in proceedings in the Federal Court of Australia
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".