Recycling precious metals from mobile phones
Bibliographic record
Abstract
The world population reached 7.5 billion inhabitants in April 2017. The number of mobile phones will reach 4.77 billion by the end of this year. Mobile phones are made of more than 50 elements. Discoveries of economically viable gold mines in the main producing countries have been slowing down significantly since the 1800s. The global surface temperature of the planet is warming at 0.17⁰C per decade relative to pre-industrial levels. The mobile phone was chosen for this thesis because it is a comprehensive unit of hazardous waste and e-waste. Mobile phones are a municipal solid waste and public health concern. The low energy and low barrier to entry recycling business this thesis envisions recycles precious metals from end of life mobile phones close to where the devices are discarded. This thesis uses system dynamics to model the exponential adoption of mobile phones and its impact in mining and CO₂e emissions. The model is the basis to calculate the return of new precious metal recycling businesses. Climate change is one of the hardest problem men has ever faced because it requires many countries to work together to establish climate centric governance and policies. Businesses are reviewing their supply chain and energy sources. This work focuses on disruptive low energy and low barrier to entry technologies to recycle precious metals from mobile phones. Local recycling businesses will create jobs and stimulate the economy in B.C., Canada, and the world.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".