Bibliographic record
Abstract
Lack of solar radiation data in the study area limited the theoretical research in simple empirical estimation formula for solar radiation and mostly for climate estimation. But sometimes we are interesting in synoptic scale and short-range climate process, in which daily solar radiation intensity needs to be estimated. This paper dealt with related problems based on the data collected in the marine survey in Xisha Island area (16°50′N, 112°20′E) in 2000 and 2002. The observation site was located in northern central South China Sea in the fringe of a reef about 300m to the southwest of Yongxing Island. Observation was done in an observatory tower. The solar radiation data were obtained twice in stages between May 8 and June 16 in 2000 (total 40days), and between April 24 and June 21 in 2002 (total 59 days). In this study, we analyzed the total solar radiation and possible influencing factors first, and then determined which factors were better for our calculation. Secondly, we built up empirical estimation formulas that mostly used cloud cover data that is fundamental for maritime meteorological study. We also tried other empirical estimation formula using total solar radiation based on cloud cover. Required corrections, such as vapor correction, were made to our calculations to obtain daily solar radiation data in the area in all possible conditions. So, firstly we carried on the contrast analysis among the solar total radiation, total cloud cover, lower cloud cover, and sunshine duration. It was found that the total solar radiation was best related to sunshine duration, followed by lower cloud cover and total cloud cover. Secondly, from the sake of calculation, we constructed a formula for getting solar total radiation, parameterized by total cloud cover, lower cloud cover and vapor, by multivariate linear regression out of the observed radiation data in 2000, with necessary error analysis and contrast analysis with the predecessors. We found that vapor correction can only improve the precision a little of total solar radiation. Thirdly, we constructed an estimation formula for solar total radiation out of all the factors above-listed. Error analysis showed that considering sunshine duration in observation area could raise estimation precision for daily solar radiation intensity, which would reduce error by 50%. Finally, we performed an independence test using Year 2002 data and confirmed the reliability of the result above. And we also apply the formula to the estimation for daily variation of the solar total radiation in phases before the SCS monsoon break. It was found that the total solar radiation increase slowly all the times. In overall, tagging item of observed sunshine duration in calculation for some area is very useful to research daily solar radiation variation, which can greatly enhance the estimation precision for daily solar radiation intensity.
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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 | 0.000 | 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 | 0.000 |
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".