Restoring the night sky darkness at Observatorio del Teide: First application of the model Illumina version 2
Bibliographic record
Abstract
ABSTRACT The propagation of artificial light into real environments is complex. To perform its numerical modelling with accuracy, one must consider hyperspectral properties of the lighting devices and their geographic positions, the hyperspectral properties of the ground reflectance, the size and distribution of small-scale obstacles, the blocking effect of topography, the lamps angular photometry and the atmospheric transfer function (aerosols and molecules). A detailed radiative transfer model can be used to evaluate how a particular change in the lighting infrastructure may affect the sky radiance. In this paper, we use the new version (v2) of the Illumina model to evaluate a night sky restoration plan for the Teide Observatory located on the island of Tenerife, Spain. In the past decades, the sky darkness was severely degraded by growing light pollution on the Tenerife Island. In this work, we use the contribution maps giving the effect of each pixel of the territory to the artificial sky radiance. We exploit the hyperspectral capabilities of Illumina v2 and show how the contribution maps can be integrated over regions or municipalities according to the Johnson–Cousins photometric bands spectral sensitivities. The sky brightness reductions per municipality after a complete shutdown and a conversion to light-emitting diodes are calculated in the Johnson–Cousins B, V, R bands. We found that the conversion of the lighting infrastructure of Tenerife with LED (1800 and 2700 K), according to the conversion strategy in force, would result in a zenith V-band sky brightness reduction of ≈0.3 mag arcsec−2.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".