Application of probabilistic approaches to the performance evaluation of building envelopes to withstand mould growth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it