REFRAME: to express differently; a look at reframing Frame Lake in Yellowknife, NT
Bibliographic record
Abstract
This graduate project examines the revitalization of Frame Lake in Yellowknife, Northwest Territories through the creative and critical lens of landscape architecture. The lake, located centrally within the city, has long been contaminated with arsenic and other pollutants, in large part due to its proximity to abandoned gold mines. The contamination of the lake has resulted in it being unsafe for activities such as fishing, swimming, wading, and berry and plant picking. The project will investigate the history of Yellowknife and potential of phytoremediation, a process that utilizes plants to clean up a contaminated environment, as a solution for restoring the ecological integrity and life of the lake to its past provision and future potential for both human and non-human use. The project will also consider the values of and cultural significance to local residents and the Yellowknives Dene. Through this examination, the project aims to not only remediate the lake but also to reframe the way we understand and value ecological assets in the north. A phased approach was taken to implement the design of the public spaces around the lake. This was done to ensure both people and native wildlife will be able to enjoy Frame Lake throughout the project implementation. Phase zero is ongoing with seasonal infrastructure employed during the long winter months in Yellowknife when Frame Lake is frozen. Phase one will occur from years 0-5, phase two from years 5-15, and phase three after 15 years of remediation when Arsenic levels should be lowered substantially, and people can once again use Frame Lake for water-based activities.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.028 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".