Evaluating suggested lengths used for cut-off planes using the effective length calculation procedure
Bibliographic record
Abstract
As building codes become more stringent in terms of thermal performance of building envelopes, and higher insulated wall assemblies are becoming more common, the heat flow due to major thermal bridges can contribute a significant portion of the total heat transfer through a building façade (Ghobadi, Moore, & Lacasse, 2019). Thermal bridge is a term used to describe a feature within a building façade which facilitates the transport of thermal energy through the envelope at a higher rate compared to the surrounding construction (ISO 10211, 2017). Thermal bridges can be found where there are changes in material properties or geometries that result in discrepancies in material thicknesses. Thermal bridges within buildings to name a few, can be found around windows, slab edges and in repeating studs within a wall. With building designers working to increase the overall energy efficiency of buildings, having tools to quantify the thermal performance of building façade during the design stage of a building is important. In quantifying the thermal performance of a building envelope during the design phase, project teams are able to identify major thermal bridges, and possibly change or adapt their design to mitigate the effects of the thermal bridge.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".