Human | Wildlife, Stitching the Fabric : Connectivity Strategies for Identified Gaps in Toronto's Ravines
Bibliographic record
Abstract
This Master’s Research Project (MRP) examines landscape connectivity strategies for the ravine system in the City of Toronto, CA. A workshop with natural environment specialists from the City of Toronto was organized to gather practitioner-based information as to which gaps should be prioritized in the ravine system. This GAP Analysis was complemented with a Geographic Information System (GIS) - based buffer analysis looking at connectable green spaces in close proximity to Environmentally Significant Areas (ESAs). Based on both the workshop and GIS analysis, 16 gaps were investigated through which 4 typologies were created. Interviews were then conducted with professionals from comparator cities: Edmonton (CA), Vancouver (CA), Minneapolis (US), Copenhagen (DK), and Stockholm (SW) to compare into how waterfront cities use policies, partnerships and design interventions to connect waterfront public lands. Based on interviews and additional policy scans, connectivity strategies were created for all 4 typologies as a means to improve landscape connectivity in the City of Toronto.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".