Author manuscript, published in "Ambiances in action / Ambiances en acte(s)- International Congress on Ambiances, Montreal 2012, Montreal: Canada (2012)" Atmosphere and heritage Documenting the architectural heritage
Bibliographic record
Abstract
Abstract. Often, when we choose buildings to be categorized as heritage, it is the building as a whole that calls for protection and not only some specific detail that is worth preserving. In this case, the values of the building not only relate to the tan-‐ gible, physical material but also to the in-‐between of the materials. This is what we identify as atmosphere, an enveloping phenomenon that surrounds and affects our sensuous system and well-‐being when we approach, enter, stay or move in a build-‐ ing. When we leave the building again we carry this atmospheric multi-‐sensory experience with us without adequate methods to describe and document it. In this paper I will introduce both new and traditional approaches to document the archi-‐ tectural heritage with the final conclusion to describe both tangible and intangible values, it requires an objective and geometrical approach as well as a subjective and phenomenological approach.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.337 | 0.074 |
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".