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Record W2031984110 · doi:10.1086/679227

Evaluation of the Sky Brightness at Two Argentinian Astronomical Sites

2014· article· en· W2031984110 on OpenAlexaboutno aff
Martin Aubé, Nicolas Fortin, Sara Turcotte, B. Garćıa, A. Mancilla, Juan Augusto Maya

Bibliographic record

VenuePublications of the Astronomical Society of the Pacific · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsnot available
Fundersnot available
KeywordsSkySky brightnessLight pollutionBrightnessNight skyRadianceCherenkov radiationRemote sensingTelescopePhysicsEnvironmental scienceAstronomyMeteorologyOpticsGeologyDetector

Abstract

fetched live from OpenAlex

Light pollution is a growing concern at many levels, especially for the astronomical community. Indeed, not only does artificial lighting veil celestial objects, it disturbs the measurement of many atmospheric phenomena. The sky brightness is one of the most relevant parameters for astronomical site selection. Our goal is to evaluate the sky brightness of two Argentinian observation sites: LEO ++ and El Leoncito. Both sites were preselected to host the Cherenkov Telescope Array. This project consists of an arrangement of many telescopes that can measure high-energy gamma ray emissions via their Cherenkov radiation produced when entering the earth's atmosphere. In this paper, we describe the measurement methods used to determine whether those sites are valuable or not. We compared our results with the sky radiance of different renowned astronomical sites (Kitt Peak, Arizona, and Mont-Mégantic, Québec, Canada). Among our results, we found that LEO ++ is a good site, however the presence of a low layer of local aerosol can introduce uncertainties in the measurements. Consequently, El Leoncito would be a better option for such an installation. This latter site shows very low sky brightness levels, which are optimal for low light detection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.259
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2014
Admission routes1
Has abstractyes

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Same venuePublications of the Astronomical Society of the PacificSame topicImpact of Light on Environment and HealthFrench-language works237,207