Methodology and Design of Field Experiments for Monitoring the Hygrothermal Performance of Wood Frame Enclosures
Bibliographic record
Abstract
Measurement of heat, air and moisture (HAM) phenomena in building assemblies under both controlled conditions and field conditions are difficult to achieve with uniform accuracy and reliability. Care is needed in selecting the measurement sensors and instrumentation to achieve an acceptable degree of accuracy. When the experiments are planned to answer specific questions or to confirm expected responses, the degree of accuracy needed is pre-determined. Proper calibration coupled with appropriate selection of materials can improve the reliability of the measurements, enhance the accuracy achieved, and ensure that the installation has the durability to survive for as long as is needed by the experiment. The field and laboratory experience of the authors in undertaking HAM measurements, particularly those involving transient conditions arising from exposure to real weather, are the basis for the recommendations provided here. The limitations in both undertaking certain measurements and in the interpretation of some data are addressed. The complex of interactions related to the driving forces and changes in material properties prevents experimenters from attributing certain outcomes to particular theoretical assumptions. However, field studies are complementary to carefully executed laboratory studies. As the accuracy of theory increases, there will be an increased need for detailed and accurate field measurements.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".