KOHONEN'S FEATURE MAPS FOR FLY ASH CATEGORIZATION
Why this work is in the frame
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Bibliographic record
Abstract
Fly ash is a common admixture used in concrete and may constitute up to 50% by weight of the total binder material. Incorporation of fly ash in Portland-cement concrete is highly desirable due to technological, economic, and environmental benefits. This article demonstrates the use of artificial intelligence neural networks for the classification of fly ashes in to different groups. Kohonen's Self Organizing Feature Maps is used for the purpose. As chemical composition of fly ash is crucial in the performance of concrete, eight chemical attributes of fly ashes have been considered. The application of simple Kohonen's one-dimensional feature maps permitted to differentiate three main groups of fly ashes. Three one-dimensional feature maps of topology 8-16, 8-24 and 8-32 were explored. The overall classification result of 8-16 topology was found to be significant and encouraging. The data pertaining to 80 fly ash samples were collected from standard published works. The categorization was found to be excellent and compares well with Canadian Standard Association's [CSA A 3000] classification scheme.
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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