Mobile phone take back learning's from various initiatives
Bibliographic record
Abstract
Takeback of obsolete electronics products has been in the focus of the environmental discussions for nearly 10 years. The European Union has published the WEEE directive which is implemented now in nearly all EU27 countries. In addition to The EU, the State of California has passed similar legislation with 34 additional states considering take back legislation. The trend continues in Canada, China and Latin America. The driving force for this is the increasing amount of consumer electronic products in the world and the desire to direct those products at the end of life to responsible recycling instead of landfill. Mobile phones are rich in metals and other natural resources allowing for economically viable recycling capturing resources such as gold, platinum, palladium and copper. One of the bottlenecks in the recycling chain has been the low return rates of the used products at the end of useful life. Consumers tend to want to keep the old products or are unaware of the take back vehicles available to them. This paper presents case studies from two countries where a variety of take back initiatives for mobile phones are in place. Raising awareness and providing easy take back options are explored as key factors in increasing the return rate of used electronics devices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".