The Effect of Agriculture on Health in Neolithic Populations in the Levant
Bibliographic record
Abstract
The purpose of this paper is to analyze the effect that the onset of agriculturalism had on the lives and health of the various Neolithic populations in the Levant during that time. Analysis of bones found at the site of Abu Hureyra (which was occupied by both hunter-gatherers and agriculturalists) show evidence for increased physical stress in the skeletons of agriculturalists, which was due to the physical stress of agriculture (Molleson 1994). Furthermore, musculoskeletal markers on Neolithic male skeletons were shown to be more symmetrical than on Natufian male skeletons. This correlates with the shift from hunting to farming (Eshed et al. 2004). It was also found that the agricultural lifestyle increased the infectious disease rate of farming populations when compared to their Natufian counterparts (Eshed et al. 2010). The shift to an agricultural lifestyle brought about many changes for dental health as well. In Neolithic populations, the rates of dental caries increased, while the wear on their teeth decreased (Eshed, Gopher, and Hershkovitz 2006; Richards 2002). This was due to the increased consumption of carbohydrates and the decreased use of teeth as tools, respectively (Eshed, Gopher, and Hershkovitz 2006; Richards 2002). Furthermore, the mandible was shown to decrease in size in the Neolithic populations when compared to Natufians (Pinhasi, Eshed, and von Cramon-Taubadel 2015). These dental changes were also seen in other areas during the agricultural shift, such as South Asia and South America (Eshed, Gopher, and Hershkovitz 2006).
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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 | 0.001 | 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) | 0.001 | 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".