State of the issue in forensic diagnosis of drowning and determination of postmortem immersion time: a review
Bibliographic record
Abstract
This review examines the current state of forensic diagnostics in drowning cases and the determination of postmortem immersion time. The analysis focuses on methods used to establish both the fact of drowning and the duration of body immersion in an aquatic environment. Specialized data on the diagnosis of drowning and the determination of postmortem immersion time were analyzed. A total of 73 sources were reviewed, including 20 Russian and 53 international publications. In determining postmortem immersion time, the primary method remains the evaluation of skin maceration degree. However, additional approaches are also explored. For instance, it has been shown that hair mass stabilizes after a certain period of water exposure, which can serve as an indicator of immersion duration, though such analysis requires specialized laboratory equipment. Furthermore, the article reviews the temperature effect on the decomposition rate, which varies with environmental conditions. Histological and microbiological methods play an important role in refining the time a body spent in water, as well as in analyzing the species composition of algae and other microorganisms involved in postmortem tissue transformation. In forensic practice, establishing the fact of drowning and estimating the duration of immersion require an integrated, multidisciplinary approach to enhance diagnostic accuracy and objectivity. However, the ambiguity in interpreting the results obtained using existing methods highlights the need for further improvement of traditional forensic examination approaches and techniques. The development and implementation of novel diagnostic technologies based on modern instrumental, molecular, and digital techniques remain highly relevant tasks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".