Environmental conditions and bodily decomposition: Implications for long term management of war fatalities and the identification of the dead during the ongoing Ukrainian conflict
Bibliographic record
Abstract
On 24 February 2022, Russia invaded Ukraine in what they referred to as a ‘special military operation’. Since then, both Russia and Ukraine have incurred heavy losses, though there remains great discrepancy between the deaths reported by both nations. In recent joint international interventions following mass casualty incidents, the majority of operations have been situated within countries in the global south; particularly within nations which do not have the appropriate medico-legal surviving infrastructures to provide the relief required in an emergency situation where high levels of deaths have been incurred. Two examples of this are the Asian Tsunami of 2004 and Haitian earthquake of 2010. The Asian Tsunami of 2004 had an epicentre 200 km west of Sumatra, with the resulting Tsunami impacting on coastal areas of Sri Lanka, Maldives, Indonesia, Malaysia, Thailand and India. It is estimated 227,000 deaths. The Haitian Earthquake 2010 of 7.0 magnitude resulted in death toll of 220,00, while the larger magnitude (7.2) earthquake of 2021 resulted in fewer fatalities of 2000. Both mass fatalities of 2004 and 2010 had considerable, multinational responses, which were challenged by scale, extent, and conditions, and resulted in operational protocols being reviewed. In Ukraine, there are robust forensic infrastructures at local and national levels, with well educated, trained and experienced staff. These infrastructures are connected to international structures such as the International Committee of the Red Cross (ICRC) and International Commission on Missing Persons (ICMP). Despite strong facilities and established procedures in Ukraine at the outset of the conflict, the intensity and nature of the fighting within densely populated areas has resulted in considerable civilian and military fatalities, inevitably this has led to temporary burials conducted during or during a lull in fighting. In areas retaken by Ukraine forces there has been considerable local effort to exhume bodies from temporary graves and to identify the remains using primarily DNA. However, the scale of this activity is disproportionate to the scale of the casualties. \n \nIn this paper, the authors provide an overview of the deaths incurred during the early weeks of the war and will attempt to illustrate the range of variables which will inform the practical response to recover and identify those killed, before they receive their final burial. It will introduce some of the organisations which have provided forensic support and will also identify emerging ethical considerations which should be monitored for the remainder of the conflict.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".