Historical, demographic, curatorial and legal aspects of the BoneMedLeg human skeletal reference collection (Porto, Portugal)
Bibliographic record
Abstract
The BoneMedLeg research project was developed to address current research concerns related to the use of skeletal reference collections for forensic purposes. These concerns were partly addressed by amassing a new reference collection which incorporates unclaimed human remains sourced from two municipal cemeteries in the city of Porto, Portugal. Amassed between 2012 and 2014 the collection was developed with permission from and in partnership with the Municipality of Porto, in a manner that is similar to that of other skeletal reference collections in Portugal. Traditionally, municipalities have bequeathed human remains that are cleared from temporary primary and secondary burial plots at local cemeteries and deemed unclaimed, to museums and universities for research purposes. The BoneMedLeg collection currently includes a total of 95 individuals, of which only 81 are fully identified (38 males and 43 females), with ages ranging from 21 days to 94 years, and a mean age of about 62 years. Years of death range from 1969 to 2003, and years of birth from 1891 to 1969. Only about half of the individuals are documented as to cause of death, which includes a considerable diversity of etiologies, from oncological to cardiovascular system disorders, and also traumatic injuries. The collection is more representative of an unskilled working class and aged population, due to one of the main sourced cemeteries disproportionately serving more socioeconomic disadvantaged communities and reflecting the demographics of the city over the past 40 years. In addition to describing the history and curatorial process of the collection in detail, this paper also discusses its broad legal framework and potential biases in its profile and composition which can inform and help plan future research projects.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".