Diet, Nutrition, and Activity at Khirbat al-Mudayna: Inferring Health in an Historical Bedouin Sample
Bibliographic record
Abstract
Chapter 2: Materials and Methods ……………..………………………………….…….. 8 2.1 Non-specific stress indicators …………....…………………………….. 14 2.2 Oral health ……………………...……………………………….…….... 15 2.3 Trauma …………………………………………………………………. 16 2.4 Degenerative joint disease ……………………………………………... 16 2.5 Specific pathological conditions ……………………………………….. 17 2.6 Long bone metrics ……………………………………………………... 18 Chapter 3: Results ………..…………………………………………………………….. 20 3.1 Khirbat al-Mudayna: Non-specific stress ……………………………… 23 3.2 Khirbat al-Mudayna: Oral health ……………………………………….26 3.3 Khirbat al-Mudayna: Trauma ……………………………………….….29 3.4 Khirbat al-Mudayna: Degenerative joint disease ……………………… 31 3.5 Khirbat al-Mudayna: Specific pathological conditions ….…………….. 34 3.6 Khirbat al-Mudayna: Long bone metrics …………….……………....… 34 3.7 Inter-site comparisons: Non-specific stress ….………………………… 35 3.8 Inter-site comparisons: Degenerative joint disease ……………...…….. 40 3.9 Inter-site comparisons: Trauma …………….………………………...... 42
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".