Analysing the impact of food waste disposal through the sink by combining the BSM1 with EASETECH modelling framework for life cycle assessment
Bibliographic record
Abstract
The newly revised European Union (EU) Circular Economy Package targets an increasing the overall recycling rate of municipal solid waste to 65% by 2030 (EC, 2019). Meeting this target will require separate collection and recycling of household food waste. Food waste disposer or kitchen grinder (FWD) has been suggested as a practical alternative to separate food waste collection. Thus, FWD has been introduced in many countries such as the US, Canada, the UK, and Sweden. To guide the decision-making process on FWDs, numerous studies have been conducted, yielding inconsistent and contradictory findings. Most studies have focused on the impact on the sewerage system, highlighting benefits such as improved resource recovery via increased biogas production or increased digestate reused as fertilizer (Kim et al., 2019a). However, those studies neglected the impact on solid waste management (i.e., reduction of food waste disposal and its treatment) and therefore offer limited information for decision making on how to handle food waste. The overall objective of this study is to examine the effect of food waste disposal on a Wastewater Resource Recovery Facility (WRRF) using benchmark simulation model (BSM2) and evaluate the waste and sewage management systems from an environmental perspective using life cycle assessment (LCA) methodology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".