Strategic Materials in the Automobile: A Comprehensive Assessment of Strategic and Minor Metals Use in Passenger Cars and Light Trucks
Bibliographic record
Abstract
A comprehensive component-level assessment of several strategic and minor metals (SaMMs), including copper, manganese, magnesium, nickel, tin, niobium, light rare earth elements (LREEs; lanthanum, cerium, praseodymium, neodymium, promethium, and samarium), cobalt, silver, tungsten, heavy rare earth elements (yttrium, europium, gadolinium, terbium, dysprosium, holmium, erbium, thulium, ytterbium, and lutetium), and gold, use in the 2013 model year Ford Fiesta, Focus, Fusion, and F-150 is presented. Representative material contents in cars and light-duty trucks are estimated using comprehensive, component-level data reported by suppliers. Statistical methods are used to accommodate possible errors within the database and provide estimate bounds. Results indicate that there is a high degree of variability in SaMM use and that SaMMs are concentrated in electrical, drivetrain, and suspension subsystems. Results suggest that trucks contain greater amounts of aluminum, nickel, niobium, and silver and significantly greater amounts of magnesium, manganese, gold, and LREEs. We find tin and tungsten use in automobiles to be 3-5 times higher than reported by previous studies which have focused on automotive electronics. Automotive use of strategic and minor metals is substantial, with 2013 vehicle production in the United States, Canada, EU15, and Japan alone accounting for approximately 20% of global production of Mg and Ta and approximately 5% of Al, Cu, and Sn. The data and analysis provide researchers, recyclers, and decision-makers additional insight into the vehicle content of strategic and minor metals of current interest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".