Inrternational Expert Group on Life Cycle Assessment for Integrated Waste Management
Bibliographic record
Abstract
The objective of sustainable waste management is to deal with society's waste in a way that is environmentally efficient, economically affordable and socially acceptable. To assess such sustainability, tools are needed which can predict the likely overall environmental burdens of any waste management system. Life Cycle Assessment (LCA) can be applied to waste management systems to assess their overall environmental burdens. The concept of Integrated Waste Management (IWM) combines waste streams, waste collection, treatment and disposal methods, with the objective of achieving environmental benefits, economic optimisation and societal acceptability. I CA tools applied to IWM systems can support the development of truly sustainable waste management systems. In general, LCA practitioners have been very much focused on the methods and the issues surrounding product life cycle development. There is now also considerable interest in the application of LCA to whole waste management systems rather than the specific waste management process used to treat a single product. The results of this area of research lead to the optimisation of complete waste management systems, which treat municipal solid waste. A forum was established in April 1998, in London, UK to support the development of LCA techniques specifically for IWM systems. There are now approximately 30 members from 10 countries (Australia, Canada, France, Germany, Ireland, Italy, Netherlands, Sweden, UK, USA) who regularly attend meetings. Membership of the group is by invitation.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.026 |
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".