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Record W4294276612 · doi:10.3847/2041-8213/ac8a4e

The JWST Early Release Observations

2022· article· en· W4294276612 on OpenAlexaff
K. M. Pontoppidan, Jaclyn Barrientes, Claire Blome, Hannah Braun, Matthew Brown, Margaret Carruthers, Dan Coe, Joseph DePasquale, Néstor Espinoza, M. García-Marín, Karl D. Gordon, Alaina Henry, Leah Hustak, Andi James, Ann Jenkins, Anton M. Koekemoer, Stephanie LaMassa, David R. Law, Alexandra Lockwood, Amaya Moro‐Martín, Susan E. Mullally, Alyssa Pagan, Dani Player, Charles Proffitt, Christine Pulliam, Leah Ramsay, Swara Ravindranath, Neill Reid, Massimo Robberto, Elena Sabbi, Leonardo Úbeda, Michael L. Balogh, Kathryn A. Flanagan, Jonathan P. Gardner, Hashima Hasan, B. K. Meinke, A. Nota

Bibliographic record

VenueThe Astrophysical Journal Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGamma-ray bursts and supernovae
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEnvironmental science

Abstract

fetched live from OpenAlex

Abstract The James Webb Space Telescope (JWST) Early Release Observations (EROs) is a set of public outreach products created to mark the end of commissioning and the beginning of science operations for JWST. Colloquially known as the “Webb First Images and Spectra,” these products were intended to demonstrate to the worldwide public that JWST is ready for science, and is capable of producing spectacular results. The package was released on 2022 July 12 and included images and spectra of the galaxy cluster SMACS J0723.3-7327 and distant lensed galaxies, the interacting galaxy group Stephan’s Quintet, NGC 3324 in the Carina star-forming complex, the Southern Ring planetary nebula NGC 3132, and the transiting hot Jupiter WASP-96b. This paper describes the ERO technical design, observations, and scientific processing of data underlying the colorful outreach products.

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)0.001
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: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.016

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 head0.014
GPT teacher head0.211
Teacher spread0.198 · 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

Citations238
Published2022
Admission routes1
Has abstractyes

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