New and Emerging Prospects for the Paleopathological Study of Starvation
Bibliographic record
Abstract
Starvation represents a significant contributor to morbidity and mortality, past and present, and is therefore of critical importance to the field of paleopathology. Scholars have previously argued that while critical to understanding past human health, starvation is often not directly observable in skeletal remains. But is this assessment still valid today? In re-evaluating this assessment, this paper discusses new developments in the analysis of (1) the “hunger osteopathies” (osteoporosis with some overlay of osteomalacia), (2) skeletal signs of arrested growth such as Harris lines and Linear Enamel Hypoplasia (LEH), and (3) carbon and nitrogen stable isotope analysis of skeletal remains. Periods of starvation are known to cause these visible and chemical alterations within skeletal remains, but these phenomena are complex, multi-etiological, and approaches to evaluate them are often fraught with a lack of standardization and specificity. An interdisciplinary approach synthesizing multiple lines of osteological and dental evidence, borrowing anatomical and medical research, and implementing new advancements in computer modeling, imaging modalities, and chemical micro-sampling may theoretically aid in inferring starvation bioarchaeologically.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".