A study of ancient glass subclassification based on K-means algorithm
Bibliographic record
Abstract
The chemical composition of glass artifacts has an important impact on ancient glass artifacts, this paper explores the various chemical compositions of the artifacts' surfaces by studying the changing law of the chemical composition ratio of the two glass artifacts' surfaces after being affected by weathering and classifying the ancient glass into subclasses. This paper firstly adopts the random forest classification method to explore how to distinguish the chemical composition of high-potassium glass and lead-barium glass to a greater extent under different weathering situations, and finds that SrO2 is the largest determinant for distinguishing the two kinds of glass after weathering, and BaO is the main indicator for determining the category before weathering. In addition, box plots were drawn in the overall dimension to preliminarily screen out reasonable chemical compositions for subclassification. Finally, the k-means clustering method was applied to establish the subclass division model, in which the k values of the model were all taken as 2. BaO was taken for subclass division in lead-barium glass, and Al2O3 was taken for subclass division in high-potassium glass, respectively.
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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 | 0.000 | 0.000 |
| 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".