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Record W2082094732 · doi:10.1063/1.4819307

High-bandwidth polarimeter for a high density, accelerated spheromak

2013· article· en· W2082094732 on OpenAlexafffund
Patrick Carle, Stephen Howard, Jordan Morelli

Bibliographic record

VenueReview of Scientific Instruments · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsGeneral Fusion (Canada)Queen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolarimeterFaraday effectPhysicsOpticsSpheromakPlasma diagnosticsMagnetic fieldFaraday cageBandwidth (computing)PlasmaPolarimetryMagnetohydrodynamicsNuclear physics

Abstract

fetched live from OpenAlex

A three-beam heterodyne polarimeter has been built to diagnose the PI-1 plasma injector at General Fusion, Inc. The polarimeter measures plasma density and Faraday rotation, which can provide estimates of magnetic field magnitude and direction. Two important calibration steps are required for the polarimeter to produce reasonable Faraday rotation signals. Beam polarization ellipticity must be measured to ensure there is a linear Faraday rotation response. In addition, the two beams travelling through the plasma must be collinear to reduce error due to differences in plasma density. Once calibrated, the Faraday rotation signals are in much better agreement with other diagnostics. For a null signal, the Faraday rotation measurement noise floor is 0.1° at 0.5 MHz bandwidth. Comparing preliminary spheromak Faraday rotation measurements to a model, the maximum error is about 0.3° at 0.5 MHz bandwidth, which is primarily due to electrical noise during the injector's capacitor discharge and limitations of the model. At a bandwidth of 0.5 MHz, the polarimeter has an axial resolution between 6 cm and 30 cm depending on the speed of the spheromak, which varies between 30 km/s and 150 km/s. The spheromak length ranges from 0.75 m to 2 m. Additional polarimeter chords will be added in future upgrades.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.844

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1570.000

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 head0.023
GPT teacher head0.280
Teacher spread0.257 · 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.

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

Citations3
Published2013
Admission routes2
Has abstractyes

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