Management Assessment of used Oil, Filters, and containers in the Canadian automotive sector using resource recovery metrics
Bibliographic record
Abstract
• Used oil, filters, and containers management are assessed in 4 Canadian provinces. • Two original indicators on resource recovery are proposed for the automotive sector. • A disparity between rising vehicle count and recovery amount is observed. • Regions with lower expenses per vehicle have higher profit margin and collection rate. • Targeted strategies towards automobile industry may improve collection rate. The efficiency of the resource recovery system in the automobile industry is not well understood. The effectiveness of resources recovery for used oil, filters and containers in four Canadian provinces were assessed from 2010 to 2022. The collection rates of resources, financial performance, and temporal changes of two original indicators: Resource Recovery Per Vehicle (RRPV), and Expenses Per Vehicle (EXPV) were examined. British Columbia and Quebec had the highest collection rates of used oil, filters, and containers (mean ranging 83.0 to 92.9 %). Despite having lowest mean collection rate of used oil (71.0 %) and filters (78.7 %), Saskatchewan has significant RRPV for used oil (20.4 L) and filters (2.12 units). Decreasing RRPV (−0.01 to −0.38) trends were identified in all jurisdictions, suggesting the need for targeted recovery strategies towards automotive sectors. A mild increasing trend of EXPV in all jurisdictions is observed (slope + 0.02 to + 0.08). Quebec exhibited the most cost-efficient resource recovery, with EXPV ranging from $2.4 to $3.3 per unit vehicle. Profit margin analysis revealed consistently high margins of 8.6 % in Quebec, contrasting with Manitoba’s lower 1.3 %. The lower profit margin may partly be due to higher administrative costs (16.1 %). The findings highlight the potential benefits of the proposed RRPV and EXPV indicators in evaluating management systems for used oil, filters, and containers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".