Confort et diversité des ambiances lumineuses en architecture; l’influence de l’éclairage naturel sur les occupants
Bibliographic record
Abstract
La recherche propose de faire la démonstration que la diversité des ambiances lumineuses, produite par l'intégration de l'éclairage naturel aux espaces intérieurs, est en mesure de créer des espaces confortables. Afin d'y parvenir, la recherche emploie un ensemble de méthodes complémentaires, notamment la cartographie positionnelle, le questionnaire fermé, la photographie, l'analyse numérique d'images et les outils développés pour le projet CRSH 2003-2007 : « Environmental Adaptability in Architecture - Towards a dynamic multi-sensory approach integrating users behavior ». Ces méthodes permettent d'étudier les ambiances lumineuses d'un espace réel, en l'occurrence le café de l'École d'architecture de l'Université Laval, et le niveau de confort perçu par ses occupants. Les résultats soulignent les différences marquées dans la perception du confort des occupants pourtant soumis aux mêmes conditions d'ambiances. Ils prouvent aussi que l'éclairage naturel est confortable puisque les ambiances lumineuses diversifiées qu'il engendre permettent aux occupants de choisir celles qui leur semblent les plus appropriées en regard à leur contexte d'activité et à leur propre définition du confort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".