Analysis of quantitative methods for rib seriation using the Spitalfields documented skeletal collection
Bibliographic record
Abstract
Accurate rib seriation is essential in forensic anthropology and bioarchaeology for determination of minimum numbers of individuals, sequencing trauma patterns to the chest, and identification of central ribs for use in age estimation. We investigate quantitative methods for rib seriation based on three metric variables: superior (anterior) costo-transverse crest height (SCTCH), articular facet of the tubercle-to-angle length (AFTAL), and head-to-articular facet length (HAFL). The sample consists of complete but unseriated sets of ribs from 133 individuals from the documented (known age and sex) and undocumented skeletal collections of Christ Church Spitalfields, London. This research confirms the results of an earlier study (Hoppa and Saunders [1998] J. Forensic. Sci. 43:174-177) and extends it with the application of two new metric traits and further analyses of sex differences. Analyses of variance showed that SCTCH and AFTAL are significantly associated (P < 0.001) with rib number. Tukey tests of pairwise rib comparisons revealed that for two dimensions (SCTCH and AFTAL), the central ribs (3rd-6th) are significantly distinct from each other (P < 0.05). Using simple ranking of either the SCTCH or AFTAL traits, the proportion of correctly identified ribs within +/-1 position was 80%, compared to initial seriation using morphological methods (Dudar [1993] J. Forensic. Sci. 28:788-797; Mann [1993] J. Forensic. Sci. 28:151-155). Significant sex dimorphism was also identified for these two traits. Analysis of the HAFL trait produced somewhat equivocal results, suggesting that this variable is not reliable for rib seriation. The variable SCTCH proves to be the most useful dimension for seriation, and shows that all but the 7th-9th ribs can be distinguished from others in the sequence, with important results for the 4th rib, where ranking allowed identification in 86% of cases, consistent with morphological methods for intact ribs.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".