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Record W2113469078 · doi:10.1017/s1743921308019005

VLBI astrometry for the NASA/Stanford gyroscope relativity mission Gravity Probe B

2007· article· en· W2113469078 on OpenAlexaff
N. Bartel, R. R. Ransom, M. F. Bietenholz, D. E. Lebach, M. I. Ratner, I. I. Shapiro, J.-F. Lestrade

Bibliographic record

VenueProceedings of the International Astronomical Union · 2007
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsYork University
Fundersnot available
KeywordsVery-long-baseline interferometryAstrometryPhysicsGeodesyParallaxProper motionAstrophysicsSatelliteQuasarVery Long Baseline ArraySystematic errorOrbit (dynamics)AstronomyStarsGeologyStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract We used VLBI observations at 8.4 GHz between 1991 and 2005 to determine the motion of the RS CVn binary IM Pegasi (HR 8703), the guide star for the NASA/Stanford gyroscope relativity mission, Gravity Probe B (GP-B). The motion was determined relative to our primary reference, the core of the quasar 3C 454.3. The stability of this core was checked relative to two other extragalactic sources, B2250+194 and B2252+172, the former of which was tied to the ICRF. The core of 3C 454.3 is stationary relative to these two sources to within 30 μas yr −1 in each coordinate. IM Pegasi's radio morphology varies, but appears to be on average centered on the primary. We estimate the proper motion of IM Pegasi with a statistical standard error (sse) of 30 μas yr −1 in each coordinate. We also estimate the parallax with a statistical standard error of 75 μas and parameters of the orbit with sse's corresponding to 110 μas on the sky. Coupled with our upper limit of three times the sse on any systematic errors in each parameter %threefold higher upper limit on the systematic error contributions to each parameter estimate, these results ensure that the uncertainty of IM Pegasi's proper motion makes only a small contribution to the uncertainty of GP-B's tests of general relativity.

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 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: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.307

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 science0.0010.000
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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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 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

Citations0
Published2007
Admission routes1
Has abstractyes

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