Material recovery from waste rubber tyres and their geoenvironmental utilisation: a review
Bibliographic record
Abstract
The escalating environmental challenges posed by waste rubber tyres (WRTs) necessitate innovative solutions to address their detrimental effects on the geoenvironment. Thus, the knowledge about the recent advancements in material recovery from WRTs, emphasising their utilisation within the framework of the United Nations Sustainable Development Goals (SDGs) and the circular economy principles, is the need of the hour. Keeping this in mind, various techniques generally used for material recovery, viz., ambient, cryogenic, waterjet, and so on, which unveil innovative approaches to reclaiming valuable resources (viz., recycled rubber, textiles, steel wires, etc.) from WRTs and various devulcanisation techniques (viz., physical, chemical, and microbial) are elaborated in this paper. In parallel, the paper explores the utilisation of the WRTs and recovered materials, highlighting their application in geotechnical and geoenvironmental engineering development projects while addressing the necessary environmental precautions and associated environmental risks/concerns. This paper incorporates circular economy principles into WRTs utilisation and focuses on achieving SDGs by promoting resource efficiency and minimising their environmental impact.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".