Modelling moisture conditions of Norway spruce (Picea abies): first validation against a global experiment [paper presented at 3rd International Conference on Moisture in Buildings 2025, Guimarães, Portugal]
Bibliographic record
Abstract
Wood used in outdoor construction is consistently subjected to wetting by precipitation. High levels of moisture content, especially if sustained over long periods, promote fungal decay and structural degradation. Predicting temporal variation of moisture content in wood exposed to rain is essential for durability assessment, and simple fit-for-purpose numerical approaches have been developed for this purpose. While these models do not fully describe the complex dynamics of free water transport, they have been shown to capture the relevant features for durability assessment. Extensive validation is however necessary to assess their applicability, robustness and limitations. This study evaluates a numerical model for moisture content prediction of Norway spruce (Picea abies) boards by comparing its outputs to measurements from 12 locations around the world, all using the same parameter settings. Overall, the model aligned with observed trends and demonstrated robustness across diverse climates, though some discrepancies likely stemmed from weather data inconsistencies and inherent simplifications. The results confirm its reliability for durability-related moisture assessments and suggest refinements to further enhance performance.
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 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".