Development of moisture reference years for assessing long-term mould growth risk of wood-frame building envelopes
Bibliographic record
Abstract
Abstract A moisture reference year (MRY) is generally used to assess the durability, or long-term performance of building envelopes within a long climatological time period, e.g. a 31 year timeframe. The intent of this paper is to develop a set of moisture reference years that can be used to assess risk to the formation of mould growth in wood-frame buildings over the long-term. The set of moisture reference years have been developed based on 15 realizations of 31-year climate data. Replicated Latin Hypercube Sampling is applied to select 15 sub-realizations with 7 representative years having different levels of moisture index (MI) from each realization. Thereafter, hygrothermal simulations are performed for a brick veneer clad wood-frame wall assembly using the 15 sub-realizations; that sub-realization which produces the highest value of maximum mould growth index over 7-year period is selected as the MRY. The selection process is then implemented for all 15 realizations of the 31-years of data sets, from which 15 sets of 7-year long MRYs are selected to represent the original 15 realizations. It is shown that the 15 sets of 7-year long MRYs can produce the same value of maximum mould growth index as well as the uncertainty as compared to the original 15 realizations having a 31-year climate data set.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".