Dynamique des ambiances lumineuses par relevés vidéo d’espaces de transition
Bibliographic record
Abstract
Natural light characterizes architecture in a complex manner, especially when considering its fluctuations and variations whenever we experience a transition or passage from a space to another. It also influences the comfort and the well-being of its occupants. This visual adaptation appears in a process that is translated into a spatio-temporal dynamics implying body movement from space to space. The literature review recognizes the lack of knowledge in the relation light-space-time. This research proposes to study this spatio-temporal relation existing between light and architectural space, to qualify an architectural promenade. It proposes to reconsider the design of transitional spaces by the spatio-temporal analysis of light, through in situ experimentation including filmic segments. The studied variables of this research take into account the qualitative and quantitative aspects of light such as luminance, time, contrast and brightness. It combines the use of a luminance-meter, a camcorder and the analysis of numerical images as a starting point for the assessment of spatio-temporal qualities of light. The resulting analysis, as well as the visualization of the dynamic experience of visual ambiances, will allow a classification of luminous transitional experiences. The architectural promenade is analyzed according to the diversity and relative intensity of luminous ambiances in relation to time, which allows the development of a descriptive analysis of visual perceptions through spatial transitions. This method of analysis and dynamic representation offers a potential to favour the design of spaces while encouraging and applying principles of luminous diversity in architecture.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".