Analyzing impact of sensor coupling on measurement representativeness of wall surface temperature
Bibliographic record
Abstract
Poor thermal-coupling between sensor and surface is one of the most important factors causing inaccuracy in measurement. Different methods had been suggested by scientists to solve this issue. Embedding the sensor into an object was one of these methods. The goal of these simulations was to assess the impact of sensor coupling on measurement representativeness of wall surface temperature. For this purpose, a cylindrically-shaped sensor was embedded into the internal surface of the wall assembly. The levels of tightness varied from 10% to 90%, which corresponded respectively from very loose to very tight conditions. Also, in this process the impact of other factors such size and materiality of the sensors’ accuracy were evaluated and discussed briefly. In this study, the results proved that as the sensor decoupled more from the surrounding environment, more accurate data was generated from it. Also, the results from the simulations signified the importance of the temperature difference between the wall surface and the indoor air temperature. The temperature difference had a direct relationship with sensor accuracy and measurement representativeness, where smaller temperature difference was associated with higher accuracy.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".