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Record W2977333106 · doi:10.1093/mnras/staa1101

Optical turbulence at Ali, China – results from the first year of lunar scintillometer observations

2020· article· en· W2977333106 on OpenAlexafffund
Paul Hickson, Feng Lu, Joschua Hellemeier, Zhi-Xia Shen, Suijian Xue, Yongqiang Yao, Bin Ma, Hualin Chen, Rui Yang

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of FinanceChinese Academy of Sciences
KeywordsScintillometerPhysicsObservatoryTurbulenceAltitude (triangle)AstronomyAtmospheric turbulenceAstrophysicsRemote sensingMeteorologyGeography

Abstract

fetched live from OpenAlex

ABSTRACT The location of an astronomical observatory is a key factor that affects its scientific productivity. The best astronomical sites are generally those found at high altitudes. Several such sites in western China and the Tibetan plateau are presently under development for astronomy. One of these is Ali, which, at over 5000 m, is one of the highest astronomical sites in the world. In order to further investigate the astronomical potential of Ali, we have installed a lunar scintillometer, for the primary purpose of profiling atmospheric turbulence. This paper describes the instrument and technique, and reports results from the first year of observations. We find that ground layer (GL) turbulence at Ali is remarkably weak and relatively thin. The median seeing, from turbulence in the range 11–500 m above ground, is 0.34 arcsec, with seeing better than 0.26 arcsec occurring 25 per cent of the time. Under median conditions, half of the GL turbulence lies below a height of 62 m. These initial results, and the high altitude and relatively low temperatures, suggest that Ali could prove to be an outstanding site for ground-based astronomy.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.001
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 head0.018
GPT teacher head0.202
Teacher spread0.184 · 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 designSimulation or modeling
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

Citations8
Published2020
Admission routes2
Has abstractyes

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