LED lighting: What We Know, What We Don't Know, What Concerns Us, and What's Next
Bibliographic record
Abstract
The electric lighting industry evolved after the introduction of Edison's incandescent lamp in the late 1800s to include new light sources such as Mercury Vapor, Fluorescent, Metal Halide, and High-Pressure Sodium. Standards were introduced to define the shapes, dimensions, and bases of lamps, for any control devices (ballasts) needed to operate the lamp, as well as methods to measure the light emitted from a fixture. Even with the introduction of light sources that did comply with the lamp standards, such the Induction Lamp (based on a Nikolai Tesla concept), the metrics for light measurement still worked.LED can best be described as a disruptive technology; it has evolved from what was an interesting concept, to the dominant light source in just over two decades. The traditional light sources, developed over the past 145 years, have been either been discontinued, or are in the process of becoming obsolete due to government regulations.The introduction of LED lighting was initially promoted by a combination of companies interested in higher profit margins, compared to traditional light sources, as well as government environmental agencies eager to adopt any new technology with significant energy savings, that would help reach their carbon reduction objectives. What wasn't well understood, or simply ignored, were any potential problems LED may pose to human health and safety, or any environmental issues.All we know with absolute certainty is that LEDs are a highly efficient light source, they are now The New Normal in Lighting [1], and there's no going back to the way things were.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".