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Record W4320498734 · doi:10.1088/1538-3873/acac53

Performance of NIRCam on JWST in Flight

2023· article· en· W4320498734 on OpenAlexafffund
Marcia Rieke, Douglas Kelly, K. A. Misselt, John Stansberry, Martha L. Boyer, Thomas G. Beatty, Eiichi Egami, Michael Florian, Thomas P. Greene, Kevin Hainline, Jarron Leisenring, Thomas L. Roellig, Everett Schlawin, Fengwu Sun, Lee Tinnin, Christina C. Williams, Christopher N. A. Willmer, Debra Wilson, Charles R. Clark, Scott Rohrbach, Brian Brooks, Alicia Canipe, Matteo Correnti, Audrey DiFelice, Mario Gennaro, J. H. Girard, G. Hartig, B. Hilbert, Anton M. Koekemoer, Nikolay Nikolov, Nor Pirzkal, A. Rest, Massimo Robberto, Ben Sunnquist, Randal Telfer, Chi Rai Wu, Malcolm Ferry, Dan Lewis, Stefi A. Baum, Charles Beichman, René Doyon, Alan Dressler, Daniel J. Eisenstein, Laura Ferrarese, K. W. Hodapp, Scott Horner, D. T. Jaffe, Doug Johnstone, John Krist, P. G. Martin, Donald W. McCarthy, Michael R. Meyer, G. H. Rieke, John T. Trauger, Erick T. Young

Bibliographic record

VenuePublications of the Astronomical Society of the Pacific · 2023
Typearticle
Languageen
FieldComputer Science
TopicTarget Tracking and Data Fusion in Sensor Networks
Canadian institutionsUniversity of TorontoHerzberg Institute of AstrophysicsUniversity of VictoriaUniversité de MontréalCanadian Institute for Theoretical AstrophysicsNational Research Council CanadaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaGoddard Space Flight CenterNational Aeronautics and Space Administration
KeywordsEnvironmental scienceAstrobiologyPhysics

Abstract

fetched live from OpenAlex

Abstract The Near Infrared Camera for the James Webb Space Telescope (JWST) is delivering the imagery that astronomers have hoped for ever since JWST was proposed back in the 1990s. In the Commissioning Period that extended from right after launch to early 2022 July, NIRCam has been subjected to a number of performance tests and operational checks. The camera is exceeding prelaunch expectations in virtually all areas, with very few surprises discovered in flight. NIRCam also delivered the imagery needed by the Wavefront Sensing Team for use in aligning the telescope mirror segments.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.220
Teacher spread0.204 · 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 designObservational
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

Citations371
Published2023
Admission routes2
Has abstractyes

Explore more

Same venuePublications of the Astronomical Society of the PacificSame topicTarget Tracking and Data Fusion in Sensor NetworksFrench-language works237,207