Combining the Locator System with WinID3--identifying victims from dental remains in a large disaster.
Bibliographic record
Abstract
Computer software programs are typically used at disaster sites to help identify victims from dental remains. Using a simulated disaster with 300 simulated victims and 105 simulated dental fragments, previous research compared two computer programs, WinID and CAPMI4, to a non-computer identification system--the Locator System (LS). LS performed best. LS requires dental professionals manually to sort antemortem and postmortem files into dental categories and then compare postmortem and antemortem files in the same category to find matches. We combined LS with the better of the two computer programs, WinID, to create a single method. This method was used by two teams of forensic odontologists to identify victims in the same simulated disaster employed in previous research. One team had 8 members and the other had 5 members. The 5-member team performed better than all previous teams and the 8-member team performed better than the 5-member team. The 8-member team was large enough to assign a different member to each category as a specialist. We make practical recommendations on identifying disaster victims from dental remains.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".