Impacts of Geographic Variation on Aluminum Lightweighted Plug-In Hybrid Electric Vehicle Greenhouse Gas Emissions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Increasing fuel prices, environmental concerns, and fuel efficiency regulations are precipitating the adoption of new vehicle construction and propulsion technologies that are sensitive to location of vehicle production and use. This sensitivity to location stands in contrast to the dominant vehicle technologies of the last 100 years. Plug-in hybrid electric powertrains and lightweight automotive aluminum are especially location sensitive as vehicle battery charging and aluminum production consume large amounts of electricity from a geographically variable electricity grid. This thesis focused on the impact of geographic variation on lifetime greenhouse gas emissions of aluminum lightweighted plug-in hybrid electric vehicles. We conducted a high resolution characterization of U.S. primary aluminum production, paying special attention to the methods used to allocate consumed electricity emissions, and performed a case study in which a plug-in hybrid vehicle’s conventional steel hood was lightweighted with aluminum. By understanding the impact of regional variations in material production and vehicle use, we wish to inform decision makers of potential hotspots within their vehicle design and material supply chain strategies. This information can help direct attention to the most impactful parts of the vehicle’s lifecycle and ensure that strategies designed to lower the lifetime greenhouse gas emissions of personal transport have the desired effect.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 it