Study on Composition Analysis and Species Identification of Glass Relics Based on the Multiple Linear Regression Model
Why this work is in the frame
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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, Na<sub>2</sub>O and Al<sub>2</sub>O<sub>3</sub> have a protective effect on weathered cultural relics, and the SiO<sub>2</sub> content decreases after weathering, while the PbO, BaO, P<sub>2</sub>O<sub>5</sub>, and CaO content increases; For high potassium glass, the content of SrO, SnO, and SO<sub>2</sub> is almost zero, and the content of Na<sub>2</sub>O remains unchanged before and after weathering. The content of SiO<sub>2</sub> increases while the content of other elements decreases. (2) The model successfully subdivided the glass subclass into four categories: low SiO<sub>2</sub>, BaO-PbO-CuO, high PBO high BaO-SO<sub>2</sub>, low BaO high PbO-SiO<sub>2</sub>, and high SiO<sub>2</sub>-PbO low BaO-Al<sub>2</sub>O<sub>3</sub>. Three types of high potassium glass: high SiO<sub>2</sub>, low SiO<sub>2</sub>, SiO<sub>2</sub>-CaO-Al<sub>2</sub>O<sub>3</sub>.
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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 it