Bibliographic record
Abstract
The US Department of Agriculture's (USDA), UVB Radiation Monitoring and Research Program 1 makes routine measurements of ultraviolet radiation at over 30 sites in the United States, Canada and New Zealand. UV measurements of total, direct and diffuse horizontal irradiances, in seven spectral channels at two nm nominal bandwidths are made with a Yankee Scientific Inc., Multiple Filter Rotating Shadow band Radiometer (UV MFRSR). A similar instrument takes measurements in the visible region with 10 nm bandwidths. The UVB group has provided, upon request, a high resolution UV product referred to as spectra, based on application of a non-linear estimation method described in Min and Harrison 2 (1998) to UV MFRSR data. This presentation examines typical problems encountered when the synthetic spectra algorithm is applied to data collected at large solar zenith angles and when the application is extended to spectral regions beyond 368 nm, the center of the longest wavelength UV MFRSR channel. In particular, the effects on derived products such as the Caldwell or Flint 3 action spectra are discussed. The useful spectral region of the algorithm has been expanded by including one or more of the datum from the visible MFRSR. This extension properly constrains the derived spectrum beyond 368 nm providing especially improved Flint action values, and can be used to estimate a PAR value if extended to include the 862 nm measurement. The extent of disagreement between measurements from LICOR PAR sensors and 'synthetic PAR' values will be presented. Planning for the next version of the synthetic spectra algorithm on the new USDA UVB web site is discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".