Bibliographic record
Abstract
R. Levinson, H. Akbari and P. Berdahl Measuring solar reflectance—Part II Measuring solar reflectance—Part II: review of practical methods Ronnen Levinson Hashem Akbari ∗ Paul Berdahl Heat Island Group Lawrence Berkeley National Laboratory April 28, 2010 Abstract A companion article explored how solar reflectance varies with surface orientation and solar position, and found that clear-sky air mass 1 global horizontal (AM1GH) solar reflectance is a preferred quantity for estimating solar heat gain. In this study we show that AM1GH solar reflectance R g,0 can be accurately measured with a pyranometer, a solar spectrophotometer, or an updated version of the Solar Spectrum Reflectometer (version 6). Of primary concern are errors that result from variations in the spectral and angular distributions of incident sunlight. Neglecting shadow, background and instrument errors, the conventional pyranometer tech- nique can measure R g,0 to within 0.01 for surface slopes up to 5:12 [23 ◦ ], and to within 0.02 for surface slopes up to 12:12 [45 ◦ ]. An alternative pyranometer method minimizes shadow errors and can be used to measure R g,0 of a surface as small as 1 m in diameter. The accuracy with which it can measure R g,0 is otherwise comparable to that of the conventional pyranometer technique. A solar spectrophotometer can be used to determine R g,0 , a solar reflectance computed by averaging solar spectral reflectance weighted with AM1GH solar spectral irradiance. Neglecting instrument errors, R g,0 matches R g,0 to within 0.006. The air mass 1.5 solar reflectance measured with version 5 of the Solar Spectrum Reflectometer can differ from R g,0 by as much as 0.08, but the AM1GH output of version 6 of this instrument matches R g,0 to within about 0.01. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".