The Tedeschi collection: A collection of documented and undocumented human skeletal remains at the Museum of Anthropology, Padua University (Italy)
Bibliographic record
Abstract
OBJECTIVE: Documented and undocumented skeletal collections offer unique windows into life in the past and are integral for research in biological anthropology. The objective of this article is to describe the documented and undocumented collections curated by the Museum of Anthropology at Padua University (Italy) to demonstrate their potential for research and encourage researchers to consider them for future projects. MATERIALS: The collection was established by Prof. Enrico Tedeschi at the beginning of 20th century, and dates to the late 19th and early 20th century. The collection consists primarily of individuals excavated from cemeteries, ossarios, or unclaimed individuals donated by hospitals or other institutions. RESULTS: Both documented and undocumented human skeletal remains are included in the collection, totalling 1,580 individuals. Associated documented information including age at death, date of death, sex, occupation, and cause of death is available in different combinations for 293 individuals of varying completeness (188 crania only, 93 crania and postcranial, 12 postcranial only). Origin and chronological period are known for the remaining 1,287 individuals. DISCUSSION: Overall, this little studied collection could be particularly useful for researchers interested in craniometrics, dentition, and 19th and 20th century health and demography in Italy. The documented skeletons will be especially useful for forensic anthropological research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".