Bibliographic record
Abstract
Old mills are at the heart of most towns in Ontario, and mark the start of the industrial boom which provided good middle class jobs for nearly everyone for over a century.However, from 2002-2012, Ontario lost over 300,000 manufacturing jobs, equivalent to 30% of the industry.1 Typical of many rural industrial communities, Smiths Falls is left with the challenges of a shrinking population and underused or vacant industrial buildings.This thesis explores innovative ways to adaptively reuse the Wood's Mill Complex in Smiths Falls in a way that positively impacts the region by reconnecting the community with their industrial heritage.The project investigates the role of social equity, cultural vitality, economic prosperity and environmental sustainability in the rehabilitation process and how this contributes to community resilience.This study of the Wood's Mill Complex could serve as a case study and guide for the sustainable rehabilitation of other vacant mills and industrial sites in rural Canada. 1 Jeff Rubin, "The Future Looks Bleak for Ontario's Manufacturing Sector," The Globe and Mail, December 30, 2013.AbstrAct Figure 0.1.Wood's Mills in historic streetscape iii I would like to thank my thesis advisors Mariana Esponda Cascajares and Mario Santana Quintero for their valuable guidance on this project.I would like to thank the Carleton Immersive Media Studio for lending me the survey equipment.A special thanks to Michael Gutland, Sujan Shrestha and Benia Semujanga for their assistance with the total station and laser scanner
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 | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".