Developing a framework for end-of-life vehicle recycling in northern Manitoba
Bibliographic record
Abstract
Scrap metal recycling is a developed industry in southern Canada, however this is not the case in northern Canada and particularly northern Manitoba. In this region, scrap metal, and particularly end-of-life vehicles (ELVs) accumulates in communities, which results in a serious waste management issue for the region. The inaction on scrap metal recycling and reuse in northern Manitoba is in fact creating sustainability issues that run counter to Manitoba’s Sustainable Development Act. To address this problem, my thesis is focused on developing an effective and efficient framework for ELV recycling for northern Manitoba. I conducted interviews with 15 community leaders and waste management specialists from communities in northern Manitoba, as well as 19 experts in ELV recycling and waste management. In addition to the interviews, a document review and a workshop, informed the development of a Framework for ELV Recycling in Northern Manitoba. Finally, I suggest the eight most important policy initiatives emerging from the research needed in order to implement the Framework.
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 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 | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".