Stone tools from the inside out: radial point distribution
Bibliographic record
Abstract
The concept of shape is central to the classification of material culture. In the case of lithic technology, archaeologists have attempted to characterize shape quantitatively and qualitatively using diverse methods ranging from manual caliper measurements and metric ratios to digital artifact scans and statistical analyses. Three-dimensional modeling has opened up new avenues for shape analysis that permit a more holistic perspective on how objects occupy space. As a result, researchers are able to explore new qualities of artifacts that were previously inaccessible through more traditional shape analyses. This paper outlines a new method for quantifying distribution of mass in lithic specimens from three-dimensional point-cloud data. Radial point distributions (RPDs) are calculated from point-filled models based on the distances of each point to the model centroid. The resulting distribution data provide a means of quantifying three-dimensional shape that is readily compared through statistical analyses. RPD calculation requires no manual specimen alignment or landmark identification, thereby removing major sources of subjectivity. It is argued that RPDs provide a means of quantifying the ‘balance’ of lithic specimens, such as handaxes, allowing researchers to explore this tactile aspect of stone tools in conjunction with more traditional visual aspects of shape.
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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 | 0.007 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".