Three-dimensional numerical analysis technique for interpreting monitored result of the exterior insulation basement systems (EIBS) experiment
Bibliographic record
Abstract
A consortium research project was initiated to determine the field performance of various thermal insulation products as applied in the exterior insulation basement system (EIBS). Initially a two-dimensional analytical tool was used to derive the thermal transmission characteristics from an array of temperature measurements monitored over a period of two years. Results immediately showed the influence of lateral heat flux between various products that differed in thermal transmission properties. Therefore, development of a three dimensional model became imperative, to resolve the order of magnitude of the lateral heat flux in the experimental set-up, and determine the actual in-situ properties of the insulation specimens. This report gives the theoretical and numerical approaches adopted to develop a three-dimensional computer model of heat transfer. The implicit Spline Method was selected for the problem solver. The applicability of the model was verified using measured data on temperature distributions at several material interfaces. Then the model was used to estimate the effect of lateral heat flows, and to determine the in-situ thermal properties of the insulation specimens. The report also records experimental details and presents validation results of the analytical approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".