Prediction of mould growth rate within building envelopes: development and validation of an improved model
Bibliographic record
Abstract
Mould growth is a common problem in building envelopes. This issue is usually caused by poor design and construction of walls and results from the difference between indoor and outdoor climatic conditions. Mould spores produced by mouldy walls may diffuse into the air, thereby affecting indoor air quality and threatening occupant health. Therefore, it is important to predict the risk of mould growth in building envelopes under various conditions. This study selected three buildings from a traditional community in Shanghai, China. First, the mould species in these building envelopes were identified. Based on the identification results, the growth rate of the corresponding genera was extracted from the literature to establish an isoline model that describes mould growth on the agar surface. In addition, the mould growth rate between and outside the isoline areas was predicted by modifying the Sautour model to relevant air temperature and humidity conditions. According to the results of the proposed model, the critical temperature and humidity that allow the growth of representative moulds from the buildings selected for this study can be expressed as φ =0.002633·cosh[0.10083·( θ -30)]+0.7153. The accuracy of the above model was verified experimentally, and the maximum relative error of the growth rate was within 25%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".