A re‐evaluation of the impact of radiographic orientation on the identification and interpretation of <scp>H</scp>arris lines
Bibliographic record
Abstract
The identification of Harris lines through radiographic analysis has been well-established since their discovery in the late nineteenth century. Most commonly associated with stress, the study of Harris lines has been fraught with inconsistent identification standards, high levels of intra- and interobserver error, and the inevitability of skeletal remodelling. Despite these methodological challenges, the use of Harris lines remains an important contributor to studies of health in archaeological populations. This research explores the radiographic process, specifically orientation and how Harris lines are initially captured for study. Using the Black Friars (13th-mid 17th centuries) skeletal sample from Denmark, 157 individuals (134 adults; 23 subadults) were radiographically analyzed in both an anterior-posterior (A-P) and medial-lateral (M-L) view for the left and right radii and tibiae. Based on the current methodological standards within the literature, it was hypothesized that the A-P view would provide the best resolution and visualization of Harris lines. The results, however, show that the number of lines visible in the M-L view were significantly higher than those visible in the A-P view; inferring that the M-L view is superior for the study of Harris lines.
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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 | 0.049 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".