Bibliographic record
Abstract
Recovering bodies from water is a common task for any medical examiner or coroner office. Unfortunately, there will be a significant postmortem interval before many of these remains are found. A thorough scene investigation must be undertaken to determine if the location of the death and that of the body recovery are the same. Decomposition in a wet environment differs from that in other settings, both in the changes that occur and the rate at which they occur. It is essential that the forensic pathologist or medicolegal death investigator recognize and appreciate the uniqueness of immersed and submerged remains. The typical decomposition changes proceed more slowly in the water, primarily due to cooler temperatures and the anaerobic environment. However, once a body is removed from the water, putrefaction will likely be accelerated. Postmortem changes are not only affected by water temperature, but also by current as well as obstacles and structures, both natural and man-made, that may interact with the remains. The anaerobic nature of decomposition for wet or submerged remains may result in adipocere formation, a unique and fascinating process that results from incomplete transformation of lipids by bacteria. Insect and animal species feeding on the remains are different for submerged bodies. Postmortem predation may cause external defects that mimic injuries and should be interpreted with care. Forensic pathologists and medicolegal death investigators must be aware of the postmortem changes that may occur with submerged and immersed bodies.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".