Intra-raw material variability and use-wear accrual: A continuing exploration
Bibliographic record
Abstract
This paper presents the latest results of an ongoing research initiative into the role of lithic raw material variability in use-wear formation. The present study continues recent exploration of wear formation associated with working dry hide. The work presented here expands on a recently published study of two raw materials from the American Southwest [San Juan Fossiliferous Chert (SJF) and Yellow Silicified Wood (YSW)] (Lerner, In Press) by presenting the analysis of two additional materials [Morrison Undifferentiated Gray Chert (MUG) and Brushy Basin Silicified Siltstone (BB)] using the same quantitative measures from the previous study (area percent, density, average intensity.) The methods used have broad geographic and temporal applicability, thus the potential for contributing to greater standardization in the quantification of archaeological use-wear.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".