Issues and Strategies for Improving Measurement Uncertainties for Solid-State Lighting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of solid-state lighting (SSL), such as light-emitting-diode (LED) products for general lighting and display applications, has increased dramatically over the past decade. However, there are significant photometric and radiometric metrological challenges with this new lighting technology. The photometric procedures and standards that have been developed for traditional lighting products, such as incandescent and compact fluorescent (CFL) lamps, do not work well for LEDs because they exhibit significantly different characteristics. This paper will discuss these differences in the spectral, geometric, and operating properties of LEDs and how they impact precise photometric measurements and associated performance metrics, such as color rendering index (CRI). The current state-of-the-art uncertainties for photometric measurements of LED lighting products is about a factor of 5 poorer than for traditional lamps, based upon the results of recent interlaboratory comparisons involving both national metrology institutes (NMIs) and accredited laboratories. Reducing the uncertainty of these measurements will have a significant impact on society—both on reducing costs due to energy savings, but also on improving overall lighting quality and performance. For these reasons, there are a number of activities being carried out both at the national and international level to address these LED measurement issues. This article will highlight the current strategies and standardization activities within both the Consultative Committee of Photometry and Radiometry (CCPR) and the International Commission of Illumination (CIE) to develop improved measurement techniques, transfer standards and metrics for the measurement and use of LED lighting in photometry, and to meet consumer needs.
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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 it