Ghost creeks: reconnecting with the lost waters of Winnipeg
Bibliographic record
Abstract
Winnipeg has been shaped socially and geographically by the waters that passed through it. Since settler development began, there has been a constant struggle between natural processes and development. The low lying land of Winnipeg was marshy in many areas and prone to flooding. In an attempt to remedy this, many of the streams which made their way from what is now farmland in the northwest, to the Red River in the east, and the Assiniboine River in the south were simply removed from our landscape. These streams are now marked only by a small dip in the road or an extra manhole cover. Although these ghost creeks have been lost to development, they have continued to have a lasting effect on our city. Every city has layers of history which have been buried under progress. By peeling back these layers we acknowledge a more complete history of our urban spaces and we create space for collective remembering of these lost landscapes. This practicum seeks to establish exemplary ways in which ghost creeks can find their place within Winnipeg’s contemporary urban fabric. This design provides site sensitive design solutions which reveal the history of the landscape, acknowledge the past, honour existing communities, and return natural ecosystem functions to the landscape. It will provide a precedent for revealing lost histories which could be built upon in a variety of contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".