Evidence for labret use in prehistory
Bibliographic record
Abstract
Aims. Compared to occlusal dental wear, labial/buccal abrasion is seldom documented in prehistoric groups. This type of wear occurs in some ethnographic-present and living populations and leaves telltale facets on non-occlusal vestibular surfaces associated with labrets. Methods and materials. For detailed macroscopic, microscopic (binocular and SEM) and thin sectioned analysis we focused on the early Neolithic site of Mehrgarh in Pakistan where labial/buccal wear is found in mostly older adult males. We studied 215 teeth from ten individuals from the site. From the literature and some personal observations, we review evidence from the Czech early Upper Paleolithic and many later sites in the Old and New Worlds. Results. For Mehrgarh macroscopic observations revealed numerous teeth with labial and buccal facets affecting nearly every tooth class. Binocular and scanning electron microscopy and one thin-sectioned tooth provided detailed information about the wear characteristics on the facets. Comparisons. The most striking parallels to wear at Mehrgarh come from recent Inuit and Northwest Coast Native Americans where labret use was frequent in males and females. Vestibular wear occurs in a wide variety of specimens from the early Upper Paleolithic to modern patients. Unlike Mehrgarh and earlier prehistoric groups, in many cases, Native American teeth are associated with the actual labrets in the graves. Conclusion. Occlusal wear or attrition caused by dental/oral manipulations where the teeth were used as tools is different, based on the resultant facets left on the teeth and micro-wear features. In prehistoric Europe, labret use extends back, at least, to the early Upper Paleolithic. As in recent humans, the use of labrets in prehistoric groups likely represents personal adornment tied to concepts of beauty and/or achieved/acquired status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".