MétaCan
Menu
Back to cohort
Record W2152225917 · doi:10.1364/ao.46.005979

Pupil plane imager for estimation of turbulence over long horizontal paths

2007· article· en· W2152225917 on OpenAlexaff
Richard B. Holmes, Williams M. Hughes

Bibliographic record

VenueApplied Optics · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsScintillationOpticsTelescopePhysicsTurbulencePupilCoherence (philosophical gambling strategy)Atmospheric opticsCoherence timeCoherence lengthPlane (geometry)Remote sensingGeologyMeteorologyMathematicsDetectorGeometryLaser

Abstract

fetched live from OpenAlex

A pupil plane imaging system, consisting of a camera and optics that image the entrance pupil of a telescope, measures scintillation induced by atmospheric turbulence. Algorithms are developed to estimate the distribution of turbulence from scintillation assuming the well known relationship between scintillation scale size and the range of turbulence layer. The algorithms were exercised using a 75 cm pupil within a 1 meter telescope located at North Oscura Peak in New Mexico, based on light from a source 52.6 km away. Estimates of the C(n)(2) profile over the path are derived using coarse range bins. From the C(n)(2) profile, an estimate of Fried's transverse coherence length was computed and compared with that from other sensors. The algorithm is tested in several ways. Error sources are discussed, including the intrinsic insensitivity of the technique to turbulence near the pupil.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.243
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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations3
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueApplied OpticsSame topicAdaptive optics and wavefront sensingFrench-language works237,207