Bibliographic record
Abstract
The relative importance of atmospheric variables such as temperature, humidity, aerosols, clouds, soiling, and snowfall, are considered for arid versus temperate regions, with specific comparison of the U.S. Southwest and Saudi Arabia with the U.S. Southeast and Ontario, Canada. Meteorological data from representative sites in these regions were obtained from NOAA NCDC, NREL TMY3, NASA AERONET, PME JRCC, and KACST to identify key atmospheric variables for each region. Estimates of the change in average monthly PV performance due to atmospheric variables are 0 to <;-6% for temperature, <;-1% to >1% for humidity, <;-0.1% to >0.1% for aerosols, 0 to <;-30% for clouds, 0 to <;-3% for soiling with cleaning, and 0 to <;-5% for snowfall. The energy density of CdTe PV systems (Wh/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) in a hot and humid climate such as Jeddah, Saudi Arabia is projected to exceed that of multi-c-Si by ~20% by 2017 due to improvements in module efficiency and differences in temperature coefficient and spectral response to humidity.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".