Bibliographic record
Abstract
The current global economy is based on the extraction of natural resources for use in products that are often disposed of after a short time. Some of the metals used in these products are becoming scarcer and more expensive, and their extraction can be associated with negative social and environmental impacts. This has prompted significant efforts to recover and recycle metals from a wide variety of post-consumer products. With a particular focus on the challenging problem of electronic waste, this essay looks at the technical, social and economic factors shaping metal re-use and recycling. Electronic waste streams can be highly enriched in metals relative to primary mined sources, and they can be considered as the richest ore deposits in the world, often containing elements that are critical for green technology applications. Failure to recover these metals not only presents a significant missed economic opportunity, but also a potential environmental threat to air, water and soil. At present, standards and practices of metal recycling and recovery are highly variable around the world, and a more coordinated effort is needed to increase their efficiency. This will require new technological approaches, alongside economic incentives and regulatory oversight. With the right intention and approaches, there is a significant opportunity to recover valuable materials from metal-rich ‘urban mines’, building robust, resilient and efficient recycling systems that are needed for a truly circular economy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.097 | 0.006 |
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; both teacher heads 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".