Bibliographic record
Abstract
Methodology and SampleI performed two analyses using skeletal remains from several sites including Cuello, Rio Azul, Iximche, Seibel, Altar de Sacrificios, Lubaantun, Altun Ha, Uaxactun, Yaltutu, Ixtonton, Chau Hiix, Tipu, and Zaculeu, and from all time periods.Both analyses were performed to see if any significant changes to the patterns that emerged occurred as a result of removing samples where sex determinations were questionable.The first analysis includes all examples of cranial and dental modifications where sex was reported, except where there was some confusion over the exact type of cranial and/or dental modification (i.e.burials 45, 105, and 136 from Cuello, and burials from tomb 23 at Rio Azul).In all other cases I simply took the word of the archaeologist at face value and included them in my analysis.Table 1 summarizes the breakdown of the total number of individuals used in this analysis in terms of period, sex, and number of individuals with cranial and/or dental modifications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".