Planning For Nature In The City: A Temporal Analysis Of Landscape Change At The Mouth Of The Don River In Toronto, Canada
Bibliographic record
Abstract
This paper critically examines the relationship between nature and the city at the mouth of the Don River in Toronto, Canada, through current and historical waterfront planning analysis at the site. An investigation of the patterns and processes restricting responsible planning of natural systems and the resulting changes to the landscape is central to this analysis, from the infilling of marshland in Ashbridge's Bay at the beginning of the 20th century, to the proposed Don Mouth Naturalization Plan (DMNP) currently in development. While historical accounts of Toronto's waterfront detail the river mouth's alteration over time, omitted from the literature is an analysis that encapsulates how the current naturalization efforts align with trends of the site's history, and what this infers about the value and management of natural systems as part of a modern-day urban waterfront. In a comparison of different time scales, this paper reflects on anthropogenic alteration at the river mouth and discusses how natural systems at the site are particularly influenced by interrelated factors of competition and economic prosperity, governance, stakeholder priorities, environmental threats, and port "functionality". The methodology used to complete this analysis consists of a literature review of urban and landscape ecology theory, an evaluation of waterfront planning history at the site, and ethnographic interviews to link historical narratives together in the context of urban-natural systems. This research reflects the realities associated with implementing naturalization within a functional urban landscape, with implications for other waterfront cities experiencing similar transitions as post-industrial landscapes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".