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Record W2066514326 · doi:10.1086/671482

Preliminary DIMM and MASS Nighttime Seeing Measurements at PEARL in the Canadian High Arctic

2013· article· en· W2066514326 on OpenAlexaboutno aff

Bibliographic record

VenuePublications of the Astronomical Society of the Pacific · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
Fundersnot available
KeywordsPearlArcticScintillationWind speedRidgeInstrumentation (computer programming)Profiling (computer programming)

Abstract

fetched live from OpenAlex

Results of deploying a differential image motion monitor (DIMM) and a DIMM combined with a multiaperture scintillation sensor (MASS/DIMM) are reported for campaigns in 2011 and 2012 on the roof of the Polar Environment Atmospheric Research Laboratory (PEARL). This facility is on a 610 m high ridge at latitude 80°N, near the Eureka weather station on Ellesmere Island, Canada. The median seeing at 8 m elevation is 0''.85 or better based on DIMM data alone, but is dependent on wind direction and likely includes a component due to the PEARL building itself. Results with MASS/DIMM yield a median seeing less than 0''.76. A semiempirical model of seeing versus ground wind speed is introduced which allows agreement between these datasets, and with previous boundary-layer profiling by lunar scintillometry from the same location. This further suggests that best 20th percentile seeing reaches 0''.53, of which typically 0''.30 is due to the free atmosphere. Some discussion for guiding future seeing instrumentation and characterization at this site is provided.

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)0.000
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 score0.143
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
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 head0.017
GPT teacher head0.205
Teacher spread0.187 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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