Precious Metals Recovery Process from Electronic Boards: Case Study of a Non-Profit Organization (QC, Canada)
Bibliographic record
Abstract
The growth in the consumption of electronic products in recent years has resulted in increasing electronic device waste. At the same time, there is a decrease in the availability of raw metals required to produce electronic boards. Recycling through the recovery of precious and critical metals contained in electronic board waste is a solution, but the processes need to be safer for the environment. This paper presents the steps that lead to investment in the development of an eco-friendly and cost-effective process for recovering precious metals from end-of-life electronic telecommunications cards. Social organizations can also become involved in the recycling of electronic cards, thus enabling the integration of marginalized people into society. We examine the case of a non-profit organization whose mission is to help people living with mental health problems through the recycling of end-of-life telecommunication devices. This recycling process must operate within constraints specific to this organization and to the employment of people with mental health issues. The literature review showed that considering ecological and economic factors, the hydrometallurgical process appeared to be a logical choice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".