A method for determining the effective longwave radiative properties of pleated draperies
Bibliographic record
Abstract
Draperies, attached to fenestration, offer a cost-effective strategy in controlling solar gain since draperies have the potential to reduce building peak load and annual energy consumption. The performance of a drapery is dependent on its solar optical and longwave radiative properties. The current study considers the determination of spatially averaged (effective) longwave radiative properties of draperies. As a first step, the longwave properties of fabrics were obtained by taking measurements with an infrared reflectometer using two backing surfaces. The measurement results enabled simple equations to be developed relating emittance and longwave transmittance to openness, emittance, and longwave transmittance of the fabric structure. In turn, the effective longwave properties of a pleated drapery are modeled using a net radiation scheme with fabric longwave properties as input. The model approximates a drapery as a series of uniformly arranged rectangular pleats. The effective longwave properties of the pleated drapery are calculated by considering an enclosure that is representative of the entire series of pleats. The longwave properties of the drapery are functions of only pleat geometry and openness of the fabric. The model compares favorably with expected trends and limits. The effect of pleating (folding ratio) is also examined.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".