Wood sorption, capillary condensation and their implications for building envelopes of wood construction
Bibliographic record
Abstract
This paper reviews the existing knowledge and a number of controversial issues concerning the relationship between wood and moisture, around basic concepts such as adsorption/desorption, capillary condensation, and the fiber saturation point. It starts with characteristics of wood micro-structure, with a focus on the pores in cell walls, followed by sorption in wood cell walls, the potential for vapour condensation at high relative humidity (RH) conditions, the measurement of wood equilibrium moisture content (EMC) at different RH levels and the concept of fiber saturation point. The discussion is then focused on the potential impact of a number of wood structure and use-related factors on the measurement of EMC under near-saturated RH conditions, particularly about the use of the pressure plate method for predicting the moisture content of low-permeance softwood species. Recommendations were provided on further studies on EMC measurement and EMC testing methods. The intent of the paper is to improve the understanding of wood properties and behaviour in building applications, and emphasise the importance of moisture management in building envelopes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".