Statistical analysis of compositional factors affecting the compressive strength of alumina-loaded epoxy (ALOX).
Bibliographic record
Abstract
Detailed statistical analysis of the experimental data from testing of alumina-loaded epoxy (ALOX) composites was conducted to better understand influences of the selected compositional properties on the compressive strength of these ALOX composites. Analysis of variance (ANOVA) for different models with different sets of parameters identified the optimal statistical model as, y{sub l} = -150.71 + 29.72T{sub l} + 204.71D{sub l} + 160.93S{sub 1l} + 90.41S{sub 2l}-20.366T{sub l}S{sub 2l}-137.85D{sub l}S{sub 1l}-90.08D{sub l}S{sub 2l} where y{sub l} is the predicted compressive strength, T{sub l} is the powder type, D{sub l} is the density as the covariate for powder volume concentration, and S{sub il}(i=1,2) is the strain rate. Based on the optimal statistical model, we conclude that the compressive strength of the ALOX composite is significantly influenced by the three main factors examined: powder type, density, and strain rate. We also found that the compressive strength of the ALOX composite is significantly influenced by interactions between the powder type and the strain rate and between the powder volume concentration and the strain rate. However, the interaction between the powder type and the powder volume concentration may not significantly influence the compressive strength of the ALOX composite.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".