Author manuscript, published in "1e conférence intercontinentale en intelligence territoriale (IT-Gatineau 2011), Gatineau: Canada (2011)" A LANDSCAPE POTENTIAL CHARACTERIZATION: SPATIAL TEMPLATE OF PEDESTRIAN AMBIENT FIELDS WITHIN THE
Bibliographic record
Abstract
Through interacting with urban and architectural spaces, pedestrian pathway‟s ambient fingerprints delineate ambiences with identifiable characteristics, thus characterizing landscape perception potentialities. Based on a measure of the surrounding open spaces using isovists, our proposal consists in building a sensory Digital Terrain models (DTM) in which the entropy of the function of radial distances is used as a third dimension. As a case study, the distribution of entropy of the Place Royale square in Nantes (France), presented in a dedicated DTM, helps us characterizing the space « intimacy quality » for each observation point. This measure constitutes a relevant indicator of spatial wealth, revealing several perspectives occurrences for a given point of view during an urban walk. This way, this geotopical process establishes a spatial potential, so as to provide to a pedestrian the ability to reach specified townscapes to be contemplated. This sensorial investigation aims to constitute a tool basis for multi-sensorial planning and design process.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.325 | 0.069 |
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".