Bibliographic record
Abstract
Mapping the dynamics of settlements is a significant task of urban planners and managers who has to analyze the potentials of the place for sustainable development. With the latest advances in GIScience, location technologies and digital displays have changed what maps look like, where we find them and what we do with them. Being one of these techniques, integration of social data with urban physical data has great promises to understand different components of the quality of place. Since measuring the performance of urban places in terms of its quality is a difficult and hard task to do, this paper will identify techniques for measuring the inner neighbourhoods' urban quality using GIScience, statistical methods such as Principal Component Analysis (PCA) and 3D visualization. The proposed methodology is tested in Montreal's inner city because of the significant problems and changes that have occurred in the city over the past two decades. Results of this paper show that 'performance of urban places' like other urban phenomena can also be mapped through 3D visualization techniques in GIScience. Such maps can be regarded as the new forms of understanding quality of our environment and thus provide new inputs for the policies to protect and regulate their "heights".
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".