A solid waste audit and directions for waste reduction at the University of British Columbia, Canada
Bibliographic record
Abstract
A novel design for a solid waste audit was developed and applied to the University of British Columbia, Canada, in 1998. This audit was designed to determine the characteristics of the residual solid waste generated by the campus and provide directions for waste reduction. The methodology was constructed to address complications in solid waste sampling, including spatial and temporal variation in waste, extrapolation from the study area, and study validation. Accounting for spatial effects decreased the variation in calculating total waste loads. Additionally, collecting information on user flow provided a means to decrease daily variation in solid waste and allow extrapolation over time and space. The total annual waste estimated from the experimental design was compared to documented values and was found to differ by -18%. The majority of this discrepancy was likely attributable to the unauthorised disposal of construction and demolition waste. Several options were proposed to address waste minimisation goals. These included: enhancing the current recycling program, source reduction of plastic materials, and/or diverting organic material to composting (maximum diversion: approximately 320, approximately 270, and approximately 1510 t yr(-1), respectively). The greatest diversion by weight would be accomplished through the diversion of organic material, as it was estimated to comprise 70% of the projected waste stream. The audit methodology designed is most appropriate for facilities/regions that have a separate collection system for seasonal wastes and have a means for tracking user flow.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".