Study on Composition Analysis and Species Identification of Glass Relics Based on the Multiple Linear Regression Model
Bibliographic record
Abstract
Antique glass products are highly susceptible to environmental influences and weathering, and their chemical composition ratios are prone to change. Given this, this article is based on integrating known data processing and mathematical methods such as comprehensive evaluation and mean analysis to establish a multiple linear regression model to explore the changes in surface chemical composition. According to the clustering analysis method, accurately classify subcategories and explore the rationality and sensitivity of the classification results. Finally, use Euclidean distance to determine the unknown category of cultural relics to be tested. The results show that: (1) For lead barium glass, Na2O and Al2O3 have a protective effect on weathered cultural relics, and the SiO2 content decreases after weathering, while the PbO, BaO, P2O5, and CaO content increases; For high potassium glass, the content of SrO, SnO, and SO2 is almost zero, and the content of Na2O remains unchanged before and after weathering. The content of SiO2 increases while the content of other elements decreases. (2) The model successfully subdivided the glass subclass into four categories: low SiO2, BaO-PbO-CuO, high PBO high BaO-SO2, low BaO high PbO-SiO2, and high SiO2-PbO low BaO-Al2O3. Three types of high potassium glass: high SiO2, low SiO2, SiO2-CaO-Al2O3.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".