Analysis of remanufacturer waste streams for electronic products
Bibliographic record
Abstract
The long-term goal of this work is to enable design of durable products that facilitates remanufacture. Remanufacturing, or recycling at a part level, involves the production-batch disassembly, restoration to like-new condition and reassembly of used products. Remanufacturing offers significant environmental benefits by retaining the energy, as well as material, embodied in the product during original manufacture, while diverting solid waste from landfills. Since the essential goal of remanufacture is to reuse parts, parts that are not reused enter the waste streams of remanufacturers and represent the ultimate obstacles to remanufacture. Study of these waste streams reveals insights about difficulties in remanufacture and how to avoid these difficulties through product design. Traditionally, remanufacturing has centered on products such as automotive parts and electrical motors, However, the growth in electronic and electrical product sectors has triggered a corresponding growth in the remanufacturing in these sectors. To support design for remanufacture in these sectors, waste streams of remanufacturers of different electronic products, namely laser-printer toner cartridges and telephones, were studied and quantified. This paper presents the results of these waste-stream analyses, including the identification of discard reasons, associated root causes for these discard reasons, and consequently, product design and other characteristics that are problematic for remanufacturing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".