Improving terminal waste diversion: Education, engagement and corporate culture at Vancouver International Airport
Bibliographic record
Abstract
Culture is a significant driver in the success of waste reduction and recycling initiatives in any region or organisation. This paper provides information on how the diversion of waste generated from passenger and operational activities (municipal waste) at the Vancouver International Airport was improved over time. For a number of years, waste diversion data indicated that Vancouver International Airport’s waste management strategies had hit a ‘ceiling’ and moving beyond an annual 36 per cent diversion rate presented challenges for the organisation. Three elements combined to overcome the barrier — a regulatory change for how waste is managed, a corporate commitment to increase diversion from terminal operations and the formal expression of accountability, teamwork and innovation as core organisational values. These components, along with a refreshed recycling culture in the Metro Vancouver region, have helped drive an additional 15 per cent improvement in waste diversion. By year end 2016, Vancouver Airport had achieved an annual diversion rate of 51 per cent, exceeding its corporate goal three years ahead of schedule.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".