Bibliographic record
Abstract
The thermal performance of three different wood framed curtain walls was analyzed and compared to the performance of two thermally broken aluminum curtain walls and one fiberglass curtain wall. U- values for the framing members and overall curtain wall, as well as condensation resistance values were obtained as per NFRC 100 through THERM/WINDOW simulation. In addition, area weighted U-value calculations were done to assess the thermal impact that glass supports had on the frame performance. Finally, whole building energy simulation was done to compare the relative performance of the system analyzed based on the building size. On average, wood curtain wall frames were found to have lower U- values than aluminum curtain wall frames and FG curtain wall frames by 59% and 14 % respectively. In addition, curtain wall sections were found to have lower U-values with wood frames than aluminum frames by an average of 14%. On the other hand, the average condensation resistance of the wood curtain walls was found to be 12% lower than aluminum curtain walls and 3% lower than FG curtain walls. The best performing curtain wall for each frame material were also modelled in OpenStudio/EnergyPlus on two different buildings and the wood curtain wall produced a TEDI that was on average 7% lower than the aluminum curtain wall. Moreover, the glass supports used in wood curtain walls were found to have a much lower impact on the U-value of the frame than the setting chairs used in aluminum curtain walls. Average overall curtain wall U-values and TEDI for the wood and FG curtain walls were found to be virtually the same.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".