Very Efficacious: Transforming the Market with the DesignLights Consortium ® Qualified Products List
Bibliographic record
Abstract
With more than 25,000 distinct products from more than 300 manufacturers, the DesignLights Consortium (DLC) Qualified Products List (QPL) is an essential database of commercial LED products for use in efficiency programs. The DLC is a project of Northeast Energy Efficiency Partnerships, a regional non-profit, and DLC members include federal agencies, state agencies, utilities, and energy efficiency programs. The DLC responds to the needs of its members by routinely updating its technical requirements and expanding into new lighting applications. This poster presents trends in the performance of products submitted for qualification since the beginning of the program in 2009. Over the course of the program, the efficacy of qualified products has improved steadily in nearly all product categories. For example, the average outdoor LED product submitted in the first quarter of 2013 is 20 % more efficacious than the average outdoor LED product submitted in the first quarter of 2011. The average indoor LED product has become 30 % more efficacious over the same period. Because the QPL presents the worst-case performance of its qualified products, the overall improvement in the market is even more impressive. In addition to these general trends, the poster also shows efficacy trends among some of the commercial market’s most important lighting categories, including the following:
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.167 | 0.070 |
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".