Testing Area‐Scale Fractal Complexity (A<scp>sfc</scp>) and Laser Scanning Confocal Microscopy (LSCM) to Document and Discriminate Microwear on Experimental Quartzite Scrapers
Bibliographic record
Abstract
Few microwear studies have been conducted on tools made from quartzite. Most rely on visual observation of microwear features using optical light microscopes and scanning electron microscopes. Quantification of microwear on quartzite tools is extremely rare, even though numerous methods to mathematically document surface roughness have been applied to other silicate tools. In this paper, laser scanning confocal microscopy (LSCM) was used to document surface roughness on four experimental scrapers made from two different subtypes of Mistassini quartzite that were used on either fresh or dry deer hide. Surface roughness data were analysed using area‐scale fractal complexity (Asfc). The results of this test case indicate that Asfc can effectively discriminate between the unused and used regions on the quartzite tools based on surface roughness, and that it can also discriminate between surface roughness produced by working dry versus fresh hides. Differences in the subtypes of Mistassini quartzite did affect surface roughness, but not significantly enough to prevent discrimination of the dry and fresh hide‐working tools. Although the use of the Asfc parameter for lithic microwear analysis requires further testing, these first results suggest it could be a reliable technique to mathematically document and discriminate wear patterns on archaeological quartzite tools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".