Vehicle Recycling, Reuse, and Recovery: Material Disposition from Current End-of-Life Vehicles
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The goal of this research was to determine and quantify today's actual end-of-life vehicle disposition rates based on their age and material content. The current facts and status of today's automotive recycling industry were sought. Disposition rates and material trends were projected using adjusted ELV age data from Duranceau and Linden's 1999 research and average materials content data from open-sources. End-of-life vehicle age and population data adjustments were used to estimate representative material compositions for the US and Canadian ELV fleet. The disposition rates were broken down by percentages of (1) part weight reused, (2) part weight remanufactured, (3) part weight recycled pre-shredder, (4) weight of recovered fluids, and (5) weight of metals recycled post shredder. The 86.3% percent material recovery established in this study was compared to the 84% reported in Paul's 2001.</div></div>
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".