Identifying and classifying thermal anomalies in Arctic houses using two infrared cameras and different metrics
Bibliographic record
Abstract
• 125 thermal anomalies were identified in 13 analyzed Arctic houses in Nunavik. • Results suggest air infiltration is the most common type of thermal anomalies. • Low-cost camera detected the same most severe anomalies as a professional one. • New Contrast-to-Noise Ratio metrics were successful in assessing thermal anomalies. • The choice of severity index influences the ranking of thermal anomalies. Buildings in Inuit communities in Canada face several challenges, such as the Arctic climate, degradation of permafrost, and important renovation needs. This significantly affects the envelope, but there is currently limited data on actual thermal defects (e.g., air leakages, thermal bridges, water infiltrations) in Arctic housing. In this work, interior thermography was employed to analyze and classify the defects in the envelope of 13 residential units in Quaqtaq, Nunavik (Canada). An average of 9.6 anomalies per unit were identified, with anomalies at wall junctions being the most common. Different metrics were then compared to rank the anomalies by severity, including the thermal index and the Contrast-to-Noise Ratio (CNR), a novel criterion in the field. It was found that the different metrics can play a complementary role in the assessment but that the ranking of the anomalies can be affected to a certain extent by the metric chosen. To facilitate the use of thermography in remote and isolated Inuit communities, the research also evaluated the potential of a low-cost infrared camera for identifying thermal anomalies in Arctic buildings compared to a high-resolution, high-cost camera. Results showed that the low-cost camera could identify the most severe anomalies but offered limited opportunities for less severe ones. The methods and results presented in the paper can inform building designers, housing managers, and renovation decision-makers to improve housing conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".