Rescue of the Material Recycler: A Case Study on the ‘End-of-Life Tyres’
Bibliographic record
Abstract
Tinna Rubber and Infrastructure Limited (TRIL) is India’s largest integrated material recycler of end-of-life tyres (ELT). It imported 70% scrap to feedstock in its manufacturing plants. The nationwide lockdown declared on 24 March 2020 posed severe initial restrictions on the movement of non-essential cargo. It affected TRIL’s 265 containers lodged at different ports in the country, thus escalating their demurrage/detention charges and storage costs. This case concerns supply chain problems faced by Shanti Swarup, TRIL Head–Procurement, owing to COVID-19 disruption. The situation was severe enough to wipe out TRIL’s annual cash profit and put its factories at risk of running out of feedstock. Scrap importers, like TRIL, were in a deadlock with shipping lines and freight stations to waive any imposed charges or avail of any monetary relief despite regular guidelines issued by the Indian government. However, Swarup’s existing and effective ‘supply chain resilience’ strategies had already made TRIL win half the battle by adopting a different approach to the deadlock problem, which relieved the situation. The case study also highlights typical risks and challenges faced by the Indian ELT recycling industry.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".