Composition analysis of ancient glass products based on logistic regression and principal component analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In order to analyze and study the composition of ancient glass products, this paper preprocessed the data and carried out statistical analysis of charts to qualitatively analyze the relationship between weathering and color, type and pattern of cultural relics, and established a Logistic regression model for quantitative analysis. There are four types of cultural relics according to the type of cultural relics and whether they are weathered. Through the evaluation model of principal component analysis, the statistical law of weathering and chemical composition content of excavated glass cultural relics is calculated according to the corresponding comprehensive score range of each kind. A multiple linear regression model was established to predict the pre-weathering component content. The correlation and difference between the chemical components of different kinds of glass relics were analyzed. The correlation coefficients of high potassium glass and lead barium glass were analyzed respectively, and the correlation coefficient heat maps were drawn. Determine the relationship between the chemical components and the differences between different chemical components.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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