Issues and Strategies for Improving Measurement Uncertainties for Solid-State Lighting
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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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 | 0.109 | 0.275 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".