Application of probabilistic approaches to the performance evaluation of building envelopes to withstand mould growth
Bibliographic record
Abstract
Probabilistic-based approaches for the performance evaluation of building envelopes to withstand mould growth have gained significant attention in recent years. In this article, a scoping review is performed to identify some current challenges and opportunities in probabilistic-based approaches. Therefore, the performance of a highly insulated wall is evaluated by applying a probabilistic-based methodology that accounts for several uncertainties and investigates their significance. A sensitivity analysis is performed according to the Morris method to understand the influence of each parameter and simplify the system representation of this case study. Deficiencies in terms of rain penetration and air leakage are accounted for. The mould growth risk is evaluated by integrating different mould models and assessment criteria. Overall, the performance of the investigated wall is found satisfactory in most of the cases, except when wind-driven rain penetration occurs. The study demonstrates that a probabilistic-based methodology enables a systematic approach to evaluate the performance of building constructions as it accounts for the involved uncertainties, provides a clear association of the microbial growth to its probability of occurrence and enables the identification of the dominant parameters, delivering more comprehensive conclusions.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".