Pre-processing of e-waste in Canada: Case of a facility responding to changing material composition
Bibliographic record
Abstract
The tracking of electronic waste (e-waste) flows through and within pre-processing facilities plays a crucial role in determining the fate of resources contained in e-waste. This study maps material and economic flows of e-waste through manual and mechanical processes at the pre-processing facility using material flow analysis. Both daily and annual material flows were accounted for, and daily flow outputs were also translated into economic flows. Each day the facility mainly processed printers and peripheral devices, laser cartridges, and refurbishable flatscreen displays. The main material outputs were glass, mixed plastics, and computer and communication wires containing copper. The most valuable products were refurbished goods and the highest revenue material was copper, whereas the highest cost item was glass from cathode ray tube (CRT) displays, due to its lead content. From 2016–2018 the facility received fewer CRT displays due to both global e-waste trends, by selling and trading CRTs to other Ontario pre-processors in exchange for flatscreen displays. This approach helped the facility to capitalize new specialized equipment for the processing of flat screens and reduced downstream leaded glass processing costs. The changing product and material profile of e-waste in Canada, and globally, needs advanced technological solutions by the pre-processors to maximize resource recovery in economically feasible manner.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".