Physical behaviour of anthropogenic light propagation into the nocturnal environment
Bibliographic record
Abstract
Propagation of artificial light at night (ALAN) in the environment is now known to have non negligible consequences on fauna, flora and human health. These consequences depend on light levels and their spectral power distributions, which in turn rely on the efficiency of various physical processes involved in the radiative transfer of this light into the atmosphere and its interactions with the built and natural environment. ALAN can affect the living organisms by direct lighting and indirect lighting (scattered by the sky and clouds and/or reflected by local surfaces). This paper mainly focuses on the behaviour of the indirect light scattered under clear sky conditions. Various interaction processes between anthropogenic light sources and the natural environment are discussed. This work mostly relies on a sensitivity analysis conducted with the light pollution radiative transfer model, Illumina (Aubé et al. 2005 Light pollution modelling and detection in a heterogeneous environment: toward a night-time aerosol optical depth retrieval method. In Proc. SPIE 2005, vol. 5890, San Diego, California, USA). More specifically, the impact of (i) the molecular and aerosol scattering and absorption, (ii) the second order of scattering, (iii) the topography and obstacle blocking, (iv) the ground reflectance and (v) the spectrum of light devices and their angular emission functions are examined. This analysis considers different behaviour as a function of the distance from the city centre, along with different zenith viewing angles in the principal plane.
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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) | 0.000 | 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) | 0.001 | 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".