How can landscape design improve the social and psychological conditions in the city improving the ecological stewardship of water?
Bibliographic record
Abstract
This thesis explores, through architectural design, the potential for human habitation to occur alongside a positive impact on the environment. Using water as the focal point, it will explore through architectural and landscape design how water resources for the city can be localized. Clean water can benefit the city both ecologically and socially. This design aspires to provide a sensual experience of water to benefit the public spaces of the city of Ottawa, while educating the public about responsible interaction with this fragile resource. Water is about balance. It has a dual symbolism as it is both an agent that cleans and an agent that purifies. Too much or too little water equals death for most creatures. The nature of water is sensitive and sacrificial. To clean, it must take the dirt as its own burden. The burden is now on us to preserve this precious resource;"... [we must] reflect on the bond the imagination creates between two kinds of stuff from which a city is made: urban space and urban water.” This research is about water as a landscape material, water as a resource, and how both of these aspects affect the urban condition.
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 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".