Field-based thermal performance analysis of a cement-stabilized, core-insulated rammed earth house in a cold climate
Bibliographic record
Abstract
This study presents an exploratory, in-depth case study on the short-term thermal and hygrothermal performance of a cement-stabilized, core-insulated rammed earth house in a cold-climate region of eastern Canada. Rammed earth construction is increasingly promoted as an eco-efficient solution for winter-dominated climates due to its thermal and moisture-regulating properties, however, empirical validation under real-world conditions remains limited. A three-day monitoring campaign was conducted under free-running winter conditions using three complementary methods: infrared thermography (IRT), surface heat flux sensing, and in-situ temperature and humidity measurements. The results reveal measurable thermal lag, reduced diurnal temperature swings, and delayed heat dissipation during unheated periods, indicating high passive heat retention. IRT demonstrated dynamic surface temperature responses to solar exposure, particularly on the south-facing wall, while heat flux data confirmed reduced transmittance through the composite earthen envelope. Indoor temperature and relative humidity remained stable throughout the monitoring period, reflecting effective hygrothermal buffering. Although limited in duration and scope, this study provides a rare, high-resolution benchmark dataset that characterizes the short-term behavior of insulated rammed earth walls in cold climates and supports future simulation-based and long-term field investigations.
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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) | 0.000 | 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) | 0.001 | 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".