Additional file 1 of Flexible electric vehicle charging and its role in variable renewable energy integration
Bibliographic record
Abstract
Additional file 1: A1: EV Policy Review at the Federal (A-1), Provincial (A-2), and Municipal (A-3) scales. A2: Vehicle scheduling parameters including facilitate passenger (A2.1) and vehicle allocation (A2.2). A3: UCC Pseudocode. A4: ERG adjustment pseudocode. A5. TAHSHA Validation. Table S1. Federal Level ZEV policies and incentives. Table S2. Provincial level ZEV policies and incentives—Governmental/Non Governmental. Table S3. Municipal level ZEV policies and incentives. Table S4. Sample person level trip schedule for Household 42. Table S5. Mode choice for each trip made in Household 42. Table S6. Facilitate passenger table for Household 42. Figure S1. Facilitate passenger resolution. Figure S2. Household vehicle allocation. Figure S3. Work activity start time comparison. Figure S4. Shopping activity start time comparison. Figure S5. School activity start time comparison. Figure S6. Other activity start time comparison. Figure S7. Return Home activity start time comparison. Figures S8. plot start time versus activity duration for Work, School, Other, and Shopping activities. Figure S9. Work activity start time vs duration for survey (left) and model (right). Figure S10. School activity start time vs duration for survey (left) and model (right). Figure S11. Other activity start time vs duration for survey (left) and model (right). Figure S12. Shopping activity start time vs duration for survey (left) and model (right). Table S7. Survey distance vs Model distance for various activity types. Figure S13. show modal split (% of trips) by period of day – Peak AM (6am – 9am), Midday (9am – 3 pm), Peak PM (3 pm – 7 pm), Late PM (7 pm – 12am) and Overnight (12am – 6am). Note that WAT indicates Walk-Access Transit (Bus in the case of Regina). Figure S14. Peak AM modal split. Figure S15. Midday modal split. Figure S16. Peak PM modal split. Figure S17. Late PM modal split. Figure S18. Overnight modal split. Figure S19. Work trip modal split. Figure S20. School trip modal split. Figure S21. Other trip modal split. Figure S22. Shopping trip modal split.
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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 | 0.002 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.823 | 0.172 |
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".