Bibliographic record
Abstract
Edmonton possesses the largest interconnected parkland in North America. Unlike other popular North American parks, there is little scholarly research on the history of this environmental asset. This paper highlights preliminary research towards a module in a historical digital atlas about Edmonton’s River Valley – a module that provides insight into waste mitigation and the management of local natural resources, where an urban problem can result in desirable amenities: such as, an urban green space. During the City of Edmonton’s formative years, locations for dumping garbage were chosen solely for inexpensive cost and convenience; impacts on the environment and human health were not concerns. Throughout the twentieth century, environmental regulations evolved to account for a myriad of issues that occur from accumulating waste – for instance, leachate and methane containment. By the 1960s, the broader environmental movement coalesced into a reaction against unnecessary pollution, but most importantly, it emphasized human health and strengthening our relationship with our environment. Through interviews with City of Edmonton Waste Management employees and an analysis of historical documents, various scholarly works, and contemporary local, and provincial,legislation, this article discusses the challenges and implications of transitioning landfills to parks in Edmonton within different eras.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 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".