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Record W2014251414 · doi:10.1117/12.2056588

Optical turbulence profiling with SloDAR in the Canadian High Arctic

2014· article· en· W2014251414 on OpenAlexafffundabout
Jérôme Maîre, Etsuko Mieda, Eric Steinbring, Richard Murowinski, James R. Graham, R. G. Carlberg, S. Wright, Nicholas M. Law, Suresh Sivanandam

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsNational Research Council CanadaUniversity of Toronto
FundersUniversity of Toronto
KeywordsArcticAdaptive opticsEnvironmental scienceTurbulenceRemote sensingGeologyAtmospheric turbulenceAtmospheric opticsThe arcticRidgeMeteorologyAtmospheric sciencesPhysicsOpticsOceanography

Abstract

fetched live from OpenAlex

The Earth's polar regions offer unique advantages for ground-based astronomical observations with its cold and dry climate, long periods of darkness, and the potential for exquisite image quality. We present preliminary results from a site-testing campaign during nighttime from October to November 2012 at the Polar Environment Atmospheric Research Laboratory (PEARL), on a 610-m high ridge near the Eureka weatherstation on Ellesmere Island, Canada. A Shack-Hartmann wavefront sensor was employed, using the Slope Detection and Ranging (SloDAR) method. This instrument (Mieda et al, this conference) was designed to measure the altitude, strength and variability of atmospheric turbulence, in particular for operation under Arctic conditions. First SloDAR optical turbulence profiles above PEARL show roughly half of the optical turbulence confined to the boundary layer, below about 1 km, with the majority of the remainder in one or two thin layers between 2 km and 5 km, or above. The median seeing during this campaign was measured to be 0.65 arcsec.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2014
Admission routes3
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdaptive optics and wavefront sensingFrench-language works237,207