Regional analysis of aluminum and steel flows into the American automotive industry
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Aluminum and steel represent the two most dominant metals in light‐duty vehicles, yet the flows of these materials into the American automotive industry have not been well characterized. This study proposes and implements a method for analyzing the flow of these metals into the automotive industry. We create a framework for performing regionally linked, sector‐specific material flow analyses and use this framework to trace flows of aluminum and steel entering the American automotive industry, focusing on flows downstream from raw material production. We show that automotive aluminum sheet and extrusions are sourced primarily from the NPCC (23%), SERC (20%), MRO (18%), and RFC (13%) North American Electric Reliability Corporation (NERC) regions, and a spatially unresolved local region within the United States and Canada (18%). We determine that primary aluminum is largely from Canada (70%), nearly all from Quebec (69%). Further upstream, alumina and bauxite originate mostly from Brazil, Australia, and Jamaica. We also show that finished automotive steel is sourced primarily from the RFC (63%) and SERC (20%) regions. The crude steel supply similarly originates mainly from the RFC (69%) and SERC (7%) regions. Upstream raw materials including coke, coking coal, iron ore, lime, and steel scrap are primarily sourced from the United States with only direct reduced iron and pig iron used in electric arc furnace steel production coming mostly from outside the United States. The framework developed here allows for increased spatial resolution of material flows, which can be used to develop more specific life cycle impact factors for life cycle assessments.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it