Decoding ’Eligibility Unknown’: transparent classification and feature-based reclassification in CAFV analysis
Bibliographic record
Abstract
This study introduces an interpretable machine learning model—ExplainableClassifier—to automate the classification of Clean Alternative Fuel Vehicle (CAFV) eligibility into three categories: Eligible, Not eligible due to low battery range, and Eligibility unknown. The model is trained on a refined dataset—reduced from over 177,000 entries to 546 unique records after de-duplication—and interpreted using SHAP and LIME to ensure transparency and control overfitting. Although the model achieves perfect performance metrics on this cleaned dataset, we emphasize dataset preparation and interpretability rather than predictive perfection. Robustness tests under perturbed conditions and DiCE-based counterfactual analysis highlight that approximately 68% of the Eligibility unknown cases can be reclassified under realistic feature adjustments. This framework supports regulatory transparency, informed policymaking, and integration with energy systems through improved EV classification, aiding adoption forecasting, load modeling, and vehicle-to-grid (V2G) planning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".