Planning for SHM in the conservation of places of faith: the case study of a Saint John, NB cathedral
Bibliographic record
Abstract
Many churches, temples, and mosques stand as cherished heritage structures, embodying the rich cultural essence of communities worldwide. Monitoring and maintaining these structures often demand unique considerations due to their distinctive features and materials. Provided in this paper is a review of sensors used in the SHM of 45 historic places of faith through discussions of sensor benefits, limitations, rational, and deployment locations across different building types. This review is contextualized via a case study of the Cathedral of the Immaculate Conception in Saint John, NB, Canada. Following a brief history of the cathedral, complications due to previous conservation efforts are discussed and the cathedral’s present state is described. Then, the sensor selection process for phases one and two of a cost-effective SHM implementation is presented. Finally, the staging and timing of sensor installation is detailed. This SHM system is expected to assist in timely damage detection and condition-based maintenance, minimising the cost of interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".