Operational Energy in Historic Religious Buildings: A Qualitative Approach
Bibliographic record
Abstract
Typically, operational energy is approached and evaluated from a quantitative point of view and, to a large extent, according to life cycle assessment (LCA). This article seeks to develop a qualitative approach to assess the past operational energy of a historic religious building in the province of Quebec, Canada. We propose a method for determining the past thermal sensation of individuals residing in a monastery by evaluating this sensation using the thermal sensation vote (TSV) related to the predicted mean vote (PMV). Doing so allows us to infer the operational temperatures and setpoints, providing an additional indicator of energy consumption. The proposed method is based on the identification and analysis of individual perceptions contained in archive documents, facilitating the reconstruction of the expressed thermal sensation and of a TSV index. The method is deployed on a prospective basis, enabling the creation of a chronological series designed to exhaustively document the thermal sensation during heating periods. This article contributes to discussions among critics who have observed a mismatch between TSV indices and PMV parameters and prognosis. It also brings us closer to a finer understanding of thermal comfort and the use/consumption of operational energy in historic religious buildings.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".