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Record W2890869258 · doi:10.7202/1051001ar

Conservation du ciel nocturne : surveillance de l’éclairage extérieur et de la pollution lumineuse au parc national et à la Réserve internationale de ciel étoilé du Mont-Mégantic

2018· article· fr· W2890869258 on OpenAlexvenueno aff
Rémi Boucher, Sarah Knefati, Camille-Antoine Ouimet

Bibliographic record

VenueLe Naturaliste canadien · 2018
Typearticle
Languagefr
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesForestryPhysicsPolitical scienceArtGeography

Abstract

fetched live from OpenAlex

Pourtant d’apparence immuable, le ciel étoilé est aujourd’hui menacé de disparition. La cause est la croissance généralisée de la pollution lumineuse, résultat de l’utilisation de dispositifs d’éclairage inadéquats. Nous présentons ici les résultats de la mesure de cette pollution obtenue par différentes approches méthodologiques sur le territoire de la Réserve internationale de ciel étoilé du Mont-Mégantic (RICEMM). La RICEMM a été créée en 2007 afin de protéger la qualité des observations astronomiques et de recherche de l’observatoire du mont Mégantic, ainsi que pour conserver les paysages étoilés exceptionnels du site. Deux aspects incontournables de la lumière artificielle nocturne ont été pris en compte : ses sources, ainsi que sa diffusion dans l’atmosphère. Les analyses démontrent que le niveau de pollution lumineuse est resté stable depuis 10 ans dans la RICEMM, tant au zénith que pour l’ensemble du ciel, et ce, malgré une tendance mondiale à la hausse des niveaux d’éclairement, l’augmentation de la population dans la périphérie du parc national du Mont-Mégantic et l’arrivée sur le marché de types de luminaires problématiques.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.013
GPT teacher head0.273
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2018
Admission routes1
Has abstractyes

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