Bodies without names: A global challenge
Bibliographic record
Abstract
A retrospective review of unidentified decedents at Salt River Mortuary, Cape Town, South Africa, 2010 -2017' by Reid et al., [1] which gives very informative insight into the work with unknown bodies in one of the busiest mortuaries of the Western Cape.Identification of unknown bodies is a challenging task that can only be successfully carried out when forensic scientists and investigating authorities are able to work together closely and effectively.If many such cases remain unsolved, the social consequences can become so severe that society loses faith in a functioning rule of law.This is currently the situation in Mexico.From the beginning of the so-called war on drugs in 2006 to the end of 2019, more than 35 000 bodies remained unidentified nationwide.No one knows how many of them may be those of persons who have been reported missingmore than 60 000 since 2006.During the past months, we have worked closely with forensic scientists and authorities to improve the identification process in the state of Jalisco, Mexico.The workload of the Instituto Jaliscience de Ciencias Forenses (IJCF) in Guadalajara, Jalisco, is comparable to that of the Salt River Mortuary, and the problems we face are very similar to those described by Reid et al. [1] On the one hand, all unidentified bodies are routinely examined by a team of forensic pathologists, odontologists and anthropologists, as well as criminalists, and samples for DNA testing are taken to obtain proper postmortem data.On the other hand, however, there is a lack of antemortem data on missing persons: information on dental status is available in few cases, and there is no nationwide DNA database for missing persons in Mexico.Furthermore, the use of antemortem fingerprints, e.g. from driving licences or electoral registers, often fails because of alleged data security problems.The comparison of existing data is therefore anything but efficient.In Mexico, families often approach forensic institutes directly to search for their missing relatives, without involving investigating authorities.The relatives leave antemortem data on their loved ones at the forensic institutes, hoping for closure.This information
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.038 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".