An Innovative System for the Treatment of Rising Dampness in Buildings Located in Cold Climates
Bibliographic record
Abstract
Signs of wetness in housing are a significant obstacle to the renovation and energy rehabilitation of old and energy-intensive heritage buildings, especially in cold climates. Thus, in order to avoid the numerous possibilities of degradation caused by the moisture transfer phenomena in the building envelope, the a disruptive aeraulic process, which focuses on the ventilation of an air gap between the thermal insulation and the wet wall, has been designed and its assessed. This system avoids the presence of liquid water at the wall surface by maintaining the hygrothermal balance within the wet wall. This enables the mechanical durability of the supporting structure, the absence of biological activity and/or frost and, hence, the durability of the thermal insulation. These issues are investigated through a case study based on a real site. Over a year of measurements, the wet wall was constantly maintained in hygroscopic balance, around 90% RH, guaranteeing the preservation of its mechanical performance, while the insulation layer was kept moisture free. In addition, the proposed model for predicting the appearance and development of biological activity demonstrated its validity, confirming experimental results.These initial results will now lead to the optimization of the aeraulic device, as well as possible use in a summer cooling context to achieve hygrothermal comfort for housing occupants.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".