UBC has a Litter-al Issue : A Comprehensive Outdoor Litter Audit at the University of British Columbia
Bibliographic record
Abstract
This University of British Columbia (UBC) outdoor litter audit encompasses a preliminary understanding of the UBC outdoor litter problem. The audit was developed in consultation with the Social Ecological Economic Development Studies (SEEDS) sustainability program, UBC Municipal Services, and Dr. Zeina Baalbaki. It successfully investigated and determined the prevalence and composition of outdoor litter with relation to high traffic areas and proximity to waste receptacles. Through developing a litter monitoring plan, implementing an outdoor waste audit, and comprehensively analyzing waste composition and density data, the audit identified key sources of litter and generated realistic solutions. The litter audit, conducted on March 23rd, 2024, revealed multiple results. It identified that cigarettes, packaging wrappers, takeout items, and receipts made up 57% of all litter collected. It also showed that out of all litter collected, litter in a 1-meter radius of waste receptacles was 10 times denser (by number of items per square meter) than litter collected within 6 meters of the receptacle. Two monitoring plans, two solutions, and multiple recommendations were developed to address the litter areas of concern. The monitoring plans will act to collect necessary information on the impact of litter on stormwater quality, and to bolster university databases on litter prevalence, location, and composition. The solutions consisted of improving accessibility and efficacy of current disposal infrastructure relating to multi-streamed waste bins and cigarette receptacles. There are significant opportunities of growth to address UBC’s outdoor litter issue – integrating continued litter monitoring with pre-existing programs, increased accessibility of outdoor paper recycling, redesigning waste receptacle openings, and considering human interaction with waste receptacles. Given that UBC has not engaged in an outdoor litter audit before, continued litter monitoring will significantly improve data robustness which can better inform future waste management decisions. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".