The Technospheric Mining of Rare Earth Elements for Sustainable Technologies
Bibliographic record
Abstract
Rare earth elements play a critical role in creating a sustainable society because they compose vital components of sustainable technologies such as wind turbines, electric and hybrid cars, and fluorescent lights. Despite their positive influence on the environment, their extraction and production is environmentally hazardous. It produces more greenhouse gases per kg of oxide than many other ores, and uses chemically intensive methods that create wastewater issues and environmental contamination. Furthermore, without the availability of these minerals, the production and advancement of these technologies would be reduced. The market is currently experiencing a gap in supply due to the decreased export quota from China. Technospheric mining provides an opportunity to reduce the environmental impact of the production of rare earth elements, as well as increase the supply for further development of sustainable technologies. \n\tTechnospheric mining looks at three main categories of stocks as a resource for rare earth elements; pre-consumer fabrics, post-consumer products, and historic urban and industrial landfills. These three categories provide numerous options for rare earth recovery. However, unless advancements in processing technologies are made, the production of recyclates will have the same negative processing effects as virgin ores. Each resource has very different properties and the technologies must address this. Furthermore, if this technique is to be successful, legislation encouraging recycling needs to be applied.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".