PETRAUL / Development of an Analytical Model of Automobile Energy Consumption During Use-Phase for Parametrized Life Cycle Assessment
Bibliographic record
Abstract
This repository contains the supplementary information files for the article "Development of an Analytical Model of Automobile Energy Consumption During the Use Phase for Parametrized Life Cycle Assessment." "Supplementary_Information_SI1_DetailedModel.docx" contains the detailed calculations and assumptions of the parametrized model. "Supplementary_Information_SI2_Preset_Configurations.xlsx" contains the pre-set configuration datasets, including assumptions and sources. "Supplementary_Information_SI4_LCA_CaseStudy_V0.1.1.xlsx" contains the datasets and unit processes for the LCA case study, comparing lightweight polycarbonate glazing with traditional glass glazing for automobiles. This model is used to generate PETRAUL, a tool for calculating automobile energy consumption for both gasoline vehicles (GV) and battery electric vehicles (BEV) based on this parametrized model and pre-set configurations. Link to the PETRAUL tool: https://petraul.streamlit.app/ The code, Jupyter Notebooks, and datasets used for computing PETRAUL, validating the model, and generating some of the pre-set configurations are available as "Supplementary_Information_SI3" at:GitHub repository: https://github.com/gabrielmagnaval/PETRAUL.git
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.074 | 0.025 |
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".