Bibliographic record
Abstract
Industrial manufacturers are increasingly being challenged to minimize the environmental impacts of their products. With rapid improvements in technology, most computers are disposed of within two years, rather than at the end of their functional life cycle of approximately 10 years. The problem of electronic waste (e-waste) is not only its growing volume but also its toxicity. The use of toxic metals and materials in computers results in environmental and health risks when computers are manufactured, incinerated, landfilled, burned or melted down during recycling. By looking at the Design for the Environment (DfE) and Extended Producer Responsibility (EPR) aspects of the European Union’s Waste Electronic and Electrical Equipment (WEEE-IT) approach compared to see what is required for sustainability design to improve the North American product stewardship approach, particularly in Manitoba, Canada. EPR places responsibility on the producer to take-back products and meet recycling rate targets. The recycling rate target of 75 % in the European Union WEEE-IT directive are more than five times the rate of recycling and recovery as the voluntary EPR has only resulted in 14 % recycling in North America, with most electronic waste (e-waste) going to landfills or incinerators. Furthermore, under the WEEE-IT’s Regulation of Hazardous Substances (RoHS) toxic chemicals like lead and mercury are
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.445 | 0.255 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".