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Record W2567687930 · doi:10.1002/2016ja022630

A dedicated H‐beta meridian scanning photometer for proton aurora measurement

2016· article· en· W2567687930 on OpenAlexafffundabout
Craig Unick, E. Donovan, Martin Connors, B. J. Jackel

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsAthabasca UniversityUniversity of Calgary
FundersCanada Foundation for Innovation
KeywordsOpticsPhotometerPhotomultiplierSplitterPhysicsFull width at half maximumDetectorRemote sensingGeology

Abstract

fetched live from OpenAlex

Abstract An instrument designed to measure the location and brightness of auroral emissions from energetic proton precipitation is described. This photometer scans from the north to south horizon with a stepper motor and mirror. The scans are configured in software for a 30 s cadence with equally spaced samples along a meridian at constant altitude. Broadband light is separated into two channels with a novel optical splitter. This splitter uses a filter that has high transmission for the signal channel and high reflection on both the long‐ and short‐wavelength sides to reflect the combined background passbands, directing each channel to its respective detector. The half‐cone angle and angle of incidence of this splitter filter allow for an overall compact optical design that also provides superior sensitivity in both signal and background channels. The signal channel is 3 nm wide full width at half maximum (FWHM) at 486.1 nm, and the background channel comprises two 3 nm wide FWHM passbands at 480 nm and 495 nm created by a single filter. Both of these channels are measured with photomultiplier tubes in photon‐counting mode. Calibrations indicate a response of around 1000 c/s per rayleigh. Data are currently acquired in 5 ms bins with a Nyquist frequency of 100 Hz. The first system (Forty‐Eight Sixty‐One (FESO)‐1) has been operating at Athabasca University since February 2014, and the second system (FESO‐2) was deployed at Lucky Lake, Saskatchewan, in October 2015. The improved sensitivity over legacy instruments and the simultaneous measurement of signal and background enable operation during intervals with dynamic electron aurora and scattered moonlight.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.102
GPT teacher head0.338
Teacher spread0.235 · 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 designOther design
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

Citations10
Published2016
Admission routes3
Has abstractyes

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