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Record W2473254869 · doi:10.1002/2016ja022699

Imaging thermal plasma mass and velocity analyzer

2016· article· en· W2473254869 on OpenAlexafffund
A. W. Yau, Andrew Howarth

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
FundersCanadian Space Agency
KeywordsSpectrum analyzerIonPlasmaAtomic physicsDrift velocityTime of flightOutflowIonosphereMaterials sciencePhysicsOpticsGeophysicsNuclear physics

Abstract

fetched live from OpenAlex

Abstract We present the design and principle of operation of the imaging ion mass and velocity analyzer on the Enhanced Polar Outflow Probe (e‐POP), which measures low‐energy (1–90 eV/e) ion mass composition (1–40 AMU/e) and velocity distributions using a hemispherical electrostatic analyzer (HEA), a time‐of‐flight (TOF) gate, and a pair of toroidal electrostatic deflectors (TED). The HEA and TOF gate measure the energy‐per‐charge and azimuth of each detected ion and the ion transit time inside the analyzer, respectively, providing the 2‐D velocity distribution of each major ionospheric ion species and resolving the minor ion species under favorable conditions. The TED are in front of the TOF gate and optionally sample ions at different elevation angles up to ±60°, for measurement of 3‐D velocity distribution. We present examples of observation data to illustrate the measurement capability of the analyzer, and show the occurrence of enhanced densities of heavy “minor” O++, N+, and molecular ions and intermittent, high‐velocity (a few km/s) upward and downward flowing H+ ions in localized regions of the quiet time topside high‐latitude ionosphere.

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)0.000
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.294
Teacher spread0.279 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

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