Testing Area‐Scale Fractal Complexity (A<scp>sfc</scp>) and Laser Scanning Confocal Microscopy (LSCM) to Document and Discriminate Microwear on Experimental Quartzite Scrapers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it