Role of Forensic Osteolgy in positive identification for Forensic investigations to solve medicolgal cases in Punjab, Pakistan
Bibliographic record
Abstract
Background: Positive identification of human skeletal remains is prime importance to solve the medicolegal cases in unidentified dead bodies. In case of mass disasters, genocide, mass graves, manslaughter and other medicolegal cases, the skeletal data provides enough information to solve the question of identification of the deceased human beings to provide justice even many years after their death. Aim: To see evidentiary value of skeletal data to determine the positive identification by using race ,age sex, stature and other parameters of identification in medicolagal cases of human skeletal remains in medicolegal forensic investigations in Punjab from 2015 to 2022. Study design: In this retrospective study 100 medico-legal cases where positive identification was done from skeletal remains in different medico-legal work performing Forensic departments of Punjab, were analyzed. Methods: In this retrospective study 100 medico-legal cases where positive identification was done from skeletal remains. This skeletal data was collected from different medico-legal cases where the identification was done on bones from five district of Punjab. This data was collected from the District Head Quarter hospitals of these cities from their medico-legal offices to conduct this research study. All the information collected were interred in predesigned Performa before analyzing the data. Descriptive statistics was implied on all the medico-legal cases for data collections and analysis of skeletal data from different bones recovered from the crime-scenes in this research study. Furthermore, standard deviation, F-statistics, and Chi test p-value were also applied to see the association of these parameters of identification with different skeletal remains with highest probability of positive identification from different bones.
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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 | 0.001 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".