Investigation of Deaths of Undocumented Border Crossers and Management of Remains
Bibliographic record
Abstract
Myriad factors influence the patterns of illegal migration of people from Mexico and Central America into the United States. The Pima County Office of the Medical Examiner (PCOME) has examined the largest number of recovered undocumented border crosser (UBC) remains of any single medical examiner jurisdiction in the United States since at least 2001. The examination and management of UBC remains is a unique process. Determination of the cause and manner of death are often compromised by advanced decomposition to the point of skeletonization. Unusual examination findings and death presentations not frequently seen in non-UBC populations are the norm. Ancillary techniques such as infrared digital photography and rehydration of mummified remains are commonly employed in the identification of these remains. Administrative challenges such as cold storage capacity, triage and handling of missing person's information, interactions with foreign governments, high media interest, identification, interment, and management of a large number of unidentified remains are ongoing issues. This review outlines the issues commonly encountered and techniques used by the PCOME to manage the remains of this vulnerable population.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".