A conceptual framework for modelling the thermal conductivity of dry green roof substrates
Bibliographic record
Abstract
The fire performance of green roofs has never been assessed numerically. In order to simulate its fire behavior, the thermal conductivity of a growing media must be determined as an important input parameter. This study characterized the thermal conductivity of a dry substrate and its prediction as a function of temperature, considering temperature effects on soil organic and inorganic constituents. Experimental measurements were made to provide basic information on thermophysical parameters of the substrate and its components. Thermogravimetric analysis was conducted to consider the decomposition of organic matter. An existing model of the thermal conductivity calculation was then applied. The results of calculated and measured solid thermal conductivity showed close values of 0.9 and 1.07 W/mK, which demonstrates that the model provided a good estimation and may be applied for green roof substrates calculations. The literature data of a temperature effect on soil solids was used to predict thermal conductivity over a range of temperatures. The results showed that thermal conductivity increased and depended on porosity and thermal properties of the soil mineral components. Preliminary validation of obtained temperature-dependent thermal conductivity was performed by experiments and numerical simulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".