Machine Learning Approach for Material Analytics and Classification – Insights Based on a Criminal Forensic Investigation Data
Bibliographic record
Abstract
Glass is a non-crystalline chalcogenide amorphous solid that is often transparent and has widespread practical, technological, and decorative usage in, for example, windowpanes, tableware, and optoelectronics.Each type of glass has different material compositions to better suit the required application.Composition of glass has various material compositions like Si, Na, Mg, Al, Ca, Ba and so on, based of which the type of glass is classified.This research work primarily focuses on assessing the capability of Machine Learning models for predicting the type of glass left in a crime scene which can further be utilized for higher levels of criminological investigations.The proposed research process incorporates collection of data set from records of forensic investigation.Further, the data is processed for any errors and processed with the aid of popular machine learning algorithms viz.Regression, decision trees, k-means clustering and random forest classifier.The proposed data set has seven different types of glass attributes with 224 sample instances are used in this study for classification.From the results it is evident that, random forest algorithm performs well with higher magnitudes of accuracy.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".