Electronic Waste and Existing Processing Routes: A Canadian Perspective
Bibliographic record
Abstract
Electrical and electronic products have become an integral part of the current economy and, with the development of newer technologies, the life span of these products are getting shorter. As a consequence, the volume of electronic waste is increasing drastically around the globe. With the implementation of new rules, regulations, and policies by the government, the landfilling of electronic waste has been reduced. The presence of valuable metals in the e-waste stream provides a major economic benefit for recycling industries but, due to the presence of hazardous materials, a proper recycling technique is required prior to the disposal of the e-waste. The total e-waste generated in Canada was 725 kt in 2014. There are several organizations currently working in various provinces to deal with the collection and recycling of e-waste. These organizations collected nearly 20% of the total e-waste generated in 2014. The collection rate for e-waste can be boosted by increasing awareness and by creating more centers to collect all kinds of e-waste. The collected e-waste is processed at local processing facilities mostly dealing with dismantling and hazardous material removal processes and then shipping the remaining material to a central location for subsequent processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.001 |
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".