Bibliographic record
Abstract
The Lake Ontario Waterfront Trail, currently stretching 350 kilometres along the shore of Lake Ontario, Canada, links 26 communities, 184 natural areas, 161 parks and promenades, 84 marinas and yacht clubs, hundreds of historic places, fairs, museums, art galleries and festivals. The Waterfront Trail is a catalyst for a new attitude and way of thinking towards the Lake Ontario waterfront and its watersheds - one that integrates ecological health, economic vitality and a sense of community. Since it was launched in 1995, the Trail has accompanied the protection of the most valued elements of the waterfront, and the transformation of under-utilized and environmentally degraded lands to vibrant places with businesses and jobs, parks and recreational facilities, green spaces, natural habitats and cultural venues and attractions. It is through the Trail that people have been mobilized to improve the waterfront as they have rediscovered the shoreline and understood the interconnections, both natural and cultural, that are so vital to its health and vitality. The Waterfront Regeneration Trust is the not-for-profit charitable organization that has been leading this large-scale greenway initiative over the past 10 years. While much has been accomplished, there remains much to do to enhance and expand the greenway. This presentation will focus on the lessons we have learned over the past decade in our involvement with more than 100 projects and what those lessons mean for the next decade of waterfront regeneration.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".