Quantifying Microwear on Experimental Mistassini Quartzite Scrapers: Preliminary Results of Exploratory Research Using <scp>LSCM</scp> and Scale‐Sensitive Fractal Analysis
Bibliographic record
Abstract
Although previous use-wear studies involving quartz and quartzite have been undertaken by archaeologists, these are comparatively few in number. Moreover, there has been relatively little effort to quantify use-wear on stone tools made from quartzite. The purpose of this article is to determine the effectiveness of a measurement system, laser scanning confocal microscopy (LSCM), to document the surface roughness or texture of experimental Mistassini quartzite scrapers used on two different contact materials (fresh and dry deer hide). As in previous studies using LSCM on chert, flint, and obsidian, this exploratory study incorporates a mathematical algorithm that permits the discrimination of surface roughness based on comparisons at multiple scales. Specifically, we employ measures of relative area (RelA) coupled with the F-test to discriminate used from unused stone tool surfaces, as well as surfaces of quartzite scrapers used on dry and fresh deer hide. Our results further demonstrate the effect of raw material variation on use-wear formation and its documentation using LSCM and RelA.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".