A Lifecycle Emissions Model (LEM): Lifecycle Emissions from Transportation Fuels, Motor Vehicles, Transportation Modes, Electricity Use, Heating and Cooking Fuels, and Materials
Bibliographic record
Abstract
This report documents changes to the methods and data in a recently revised version of the greenhouse-gas emissions model originally documented in Emissions of Greenhouse Gases from the Use of Transportation Fuels and Electricity, ANL/ESD/TM-22, Volumes 1 and 2, Center for Transportation Research, Argonne National Laboratory, Argonne (ANL), Illinois (DeLuchi, 1991, 1993). The revised Lifecycle Emissions Model (LEM) calculates energy use, air-pollutant emissions, and CO2-equivalent emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), chlorofluorocarbons (CFC-12), nitrogen oxides (NOx), carbon monoxide (CO), non-methane organic compounds (NMOCs) weighted by their ozone-forming potential, sulfur oxides (SOx), hydrogen (H2), and particulate matter (PM) from the lifecycle of fuels and materials for a wide range of transportation modes, vehicles, and fuels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".