Planckian regression temperature for least spectral error and least CIELAB error
Why this work is in the frame
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Bibliographic record
Abstract
The correlated color temperature (CCT) provides a simple and useful descriptor for a given spectral power distribution as well as an approximation of the full spectrum of the measured illuminant. But typically, the CCT is calculated on the basis of distance in the chromaticity plane. Here we suggest that, while familiar, this metric is not the most effective for actually generating a useful spectral approximation. Given the recent interest in whole-spectrum calculations, we consider what optimization would be most sensible for identifying the nearest Planckian in terms of the whole-spectrum RMS error; in that case, we are calculating a variant of the distribution temperature, another simple descriptor. This effectively means that instead of one value T, we instead describe a spectrum in terms of both T and an intensity I. In general, we wish to balance the need for (i) a best mapping of the whole spectrum and (ii) the smallest CIELAB error. As a first step, we show how to calculate the spectrum analytically in the case when RMS spectral-error minimization is the sole goal. Generalizing, we consider an optimization that tries to minimize a balance of RMS and CIELAB error, leading to a family of solutions. Finally, we suggest a specific optimization that arguably forms a best trade-off of these two objectives, which we denote the Planckian regression temperature. Results are shown for some standard test illuminants and then for a further 102 measured spectra, with results separately reported for fluorescent and nonfluorescent illuminants.
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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