Benefiting from Industrial Heritage: Toronto Distilery District Urban Transformation Example
Bibliographic record
Abstract
It is a common fact that the confinement and to become unfunctional of the historical factories due to they have remained behind of technological improvement, not been managed effectively and polluted to the environment. However, their falling into disuse the demolition and the destruction of these buildings cannot be accepted. A lot of factories in Türkiye that have been able to reach today from the industrial background had been demolished for their land value, and a few of them had been re-functioned by transforming. These plants as contributes to the economic development of the countries and bears the stamp of the past, also could be re-evaluated and transferred to future generations by preserving their original identities. With the transformation of a historical plant, not only an industrial heritage would be protected but also this act would contribute to the economic development and cultural significance of the urban, and improve the quality of life. On this issue, there are lots of examples in Western countries. In this study, a succesful transformation sample from Canada-Toronto has been handled. In Distillery Region where has a significant role in the establishment, enrichment and physical embodiment of the city of Toronto, the regeneration/transformation process has been achieved thus the region has been redounded to tourism industry. In the study, the development of Distilery and its meaning for the urban is stated, the transformation process and its effect on urban is discussed and at this issue some suggestions are made while determining the deficiencies in Türkiye.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".