Bibliographic record
Abstract
This thesis explores the preservation and adaptive re-use of Ottawa's Lemieux Island Water Purification Plant, incorporating new programs -biological water purification, aquaponics, water-based recreation, and education.Recently, rising water levels in the Ottawa River have led to flooding and threatened the operations at this plant.Extreme weather conditions, along with the public's accessibility to new options in water treatment in the future, may mean that the original function of the plant will become obsolete, offering opportunities for the facilities to be repurposed.Based on current scientific information about increasing precipitation and ongoing climate instability, as well as on the existing flood patterns of the Ottawa River, the thesis design incorporates changes to the island's topography in order to protect the historical buildings that are at lower elevations from flooding.Simultaneously, I propose a utilization of the industrial settling and filtering buildings with large water-holding capacities at higher elevations to test and study methods of bioremediation of water from the Ottawa River.This reimagined filtration plant will supply various other programs within the adapted historical building that include recreation, agriculture, and education.The main water bioremediation method used is Dr. John Todd's "Living Machine" system, that mimics the cleansing function of natural wetlands.The recreational element of the project includes natural swimming pools with separate water cleansing components.The agricultural element of the project centers on an aquaponics system.The educational
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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 | 0.000 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".