Measuring solar reflectance Part I: Defining a metric that accurately predicts solar heat gain
Bibliographic record
Abstract
R. Levinson, H. Akbari and P. Berdahl Measuring solar reflectance—Part I Measuring solar reflectance—Part I: defining a metric that accurately predicts solar heat gain Ronnen Levinson Hashem Akbari ∗ Paul Berdahl Heat Island Group Lawrence Berkeley National Laboratory April 28, 2010 Abstract Solar reflectance can vary with the spectral and angular distributions of incident sunlight, which in turn depend on surface orientation, solar position and atmospheric conditions. A widely used solar reflectance metric based on the ASTM Standard E891 beam-normal solar spectral irradiance underestimates the solar heat gain of a spectrally selective “cool colored” surface because this irradiance contains a greater fraction of near-infrared light than typically found in ordinary (unconcentrated) global sunlight. At mainland U.S. latitudes, this metric R E891BN can underestimate the annual peak solar heat gain of a typical roof or pavement (slope ≤ 5:12 [23 ◦ ]) by as much as 89 W m −2 , and underestimate its peak surface temperature by up to 5 K. Using R E891BN to characterize roofs in a building energy simulation can exaggerate the economic value N of annual cool-roof net energy savings by as much as 23%. We define clear-sky air mass one global horizontal (“AM1GH”) solar reflectance R g,0 , a simple and easily measured property that more accurately predicts solar heat gain. R g,0 predicts the annual peak solar heat gain of a roof or pavement to within 2 W m −2 , and overestimates N by no more than 3%. R g,0 is well suited to rating the solar reflectances of roofs, pavements and walls. We show in Part II that R g,0 can be easily and accurately measured with a pyranometer, a solar spectrophotometer or version 6 of the Solar Spectrum Reflectometer. Akbari’s current address: Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Quebec, Canada. In press at Progress in Solar Energy April 28, 2010
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.002 |
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".