Research Methods for Classification and Identification of Ancient Glass Types
Bibliographic record
Abstract
Ancient glass is susceptible to the influence of the environment of the burial site and then produce weathering, weathering will lead to changes in the proportion of its color and chemical composition, this paper analyzes the data of high-potassium glass and lead-barium glass, research on the weathering law of the glass artifacts, and classify and identify the type of glass. In order to classify the types of glass, this paper determines the best ccp_alpha of CART algorithm is located at [0,0.39296057] by cost pruning method, reduces the impurity of the classified tree to 0, and finds that the main difference between the classification of high-potassium glass and lead-barium glass lies in the content of PbO. The chemical compositions of different glasses are subclassified by K-means, and the number of nests of subclassified high-potassium glass and lead-barium glass is determined to be 4 and3 respectively with the help of SSE coefficients and profile coefficients, and the detailed subclassification is realized by CART algorithm. On the basis of the above, the prediction accuracy of Al-A8 glass types was accomplished by the perceptual machine model with 100% accuracy, and the results showed that the model stability and accuracy were high.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".