Inspection of Feasible Calibration Conditions for <scp>UV</scp> Radiometer Detectors with the <scp>KI</scp>/<scp>KIO</scp><sub>3</sub> Actinometer
Bibliographic record
Abstract
UV radiometers are widely employed for irradiance measurements, but their periodical calibrations not only induce an extra cost but also are time-consuming. In this study, the KI/KIO3 actinometer was applied to calibrate UV radiometer detectors at 254 nm with a quasi-collimated beam apparatus equipped with a low-pressure UV lamp, and feasible calibration conditions were identified. Results indicate that a washer constraining the UV light was indispensable, while the size (10 or 50 mL) of a beaker containing the actinometer solution had little influence when a proper washer was used. The absorption or reflection of UV light by the internal beaker wall led to an underestimation or overestimation of the irradiance determined by the KI/KIO3 actinometer, respectively. The proper range of the washer internal diameter could be obtained via mathematical analysis. A radiometer with a longer service time showed a greater calibration factor. To minimize the interference from the inner wall reflection of the collimating tube, calibrations should be conducted at positions far enough away from the tube bottom. This study demonstrates that after the feasible calibration conditions are identified, the KI/KIO3 actinometer can be applied readily to calibrate UV radiometer detectors at 254 nm.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".