What’s the Dirt on Composting? An Exploration on Improving Personal Composting for Residents Living in Downtown Toronto High Rises
Bibliographic record
Abstract
<p>Composting is an effective way for anyone to divert their organic waste from landfills. Unfortunately, in high density cities, such as Toronto, composting rates can be as low as 22% (Government of Canada, 2015). This is an opportunity to make a positive impact on the planet while making a simple change. The literature that supports improving composting is focusing on how compost works and what can be done to improve the process, how education and convenience plays a role, and can design thinking help solve improving composting by approaching the problem as a design problem. This study asks residents living in downtown Toronto what their composting habits are like whether they compost. First the participants were given a survey then had a follow up interview that would give a deeper understanding to how composting can be a challenge and how they feel it can be improved. The common theme among the participants was they needed better education on what goes in the composting bin, transparency with how the city handles the compost waste, and improving convenience of the compost waste disposal. A proposed solution is an app that helps the user compost despite whatever their current situation is. For example, if a user does not know what goes in their compost bin, the app will clearly list what goes in the bin depending on what region they live in.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".