GEOLOGICAL ASPECTS OF URBAN PERSONAS: A COMPUTATIONAL AI PIPELINE FOR THE MULTIDIMENSIONAL CHARACTERIZATION OF CITY STREETS IN THE PROVINCE OF QUÉBEC
Bibliographic record
Abstract
City streets tend to be conceptualized from a viewpoint of a particular or general observer. Smaller-scale studies and interventions focus on the perspectival and photographic views of particular streets, providing rich but highly nuanced information. On a larger scale, streets are elements within orthographic and axonometric views from a general observer placed at infinity, situating them in a standardized way within a single coordinate space. These representations require a selection of what will be shown. As research into the urban subsurface has observed, disciplines whose work is directly impacted by the geological conditions of a site are rarely confronted by geology in the early phases of the design process. The urban underground tends to remain hidden. Long-term holistic and multidimensional planning, which includes the subsurface as one among many characterizations of the location of a project, is complicated by the increasing number of dimensions that compete for priority in the politics of territorial transformation. Geographical information systems (GIS) have helped centralize and standardize heterogeneous data sources. These information management platforms have been accompanied by work conducted on how to synthesize that data and present it on a large scale. What has yet to be explored extensively is how geology can be looked at through a high-dimensional data model that harnesses the pattern-seeking capabilities of machine-learning techniques. From the standpoint of our contemporary information technology, artificial intelligence should be able to provide an impersonal viewpoint from which to look at all the available data for a street on a planetary scale.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".