Classification of Gasoline Grades Using Compositional Data and Expectation–Maximization Algorithm
Bibliographic record
Abstract
This work demonstrates the application of an expectation–maximization (EM) algorithm in classifying gasoline samples belonging to different commercial grades based on gas chromatography (GC) and gas chromatography–mass spectrometry (GC–MS) compositional data. The classification process was based on an “optimal” subset of compositional variables, which were identified by means of a variable reduction method that maintained a multivariate data structure. The EM algorithm was then applied on this variable subset to determine the Gaussian model parameters that best described the data. Initially, an evaluation of the methodology was carried out on published GC–MS data of 88 Canadian gasoline samples, and the results from our study were compared to the results that were already presented in past literature. The methodology was subsequently tested on GC data from 74 Greek gasoline samples analyzed in our laboratory. The conjunction of variable reduction with the EM algorithm has proven to be a successful and reliable classification tool for gasoline samples belonging to different commercial grades (premium, regular, winter, and summer) in both data sets.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".