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Record W3127194883

A Long-Term Vision for Space-Based Interferometry

2019· article· en· W3127194883 on OpenAlexaff
Stephen Rinehart, Jonathan W. Arenberg, Ellyn K. Baines, Christine Chen, L. Ilsedore Cleeves, M. J. Creech‐Eakman, Daniel A. Dale, W. C. Danchi, D. Farrah, Roser Juanola-Parramon, Stefan Kraus, J. S. Knight, Sarah Lipscy, David Leisawitz, Meredith A. MacGregor, Bertrand Mennesson, John D. Monnier, David A. Naylor, Rachel O'Connor, Aki Roberge, G. Savini, H. R. Schmitt, M. Sewiło, Locke D. Spencer, Theo A. ten Brummelaar, Gerald van Belle, H. W. Yorke

Bibliographic record

VenueBulletin of the American Astronomical Society · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsAngular resolution (graph drawing)ExoplanetInterferometryAstronomical interferometerPlanetPhysicsEvent (particle physics)Space (punctuation)AstronomyTerm (time)AstrophysicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

The processes leading to the formation of planets; the extreme physics occurring near the event horizon of black holes; detailed studies of exoplanets through spectral-spatial mapping: new and unique insights into the physical processes involved across nearly the whole gamut of astrophysics await discovery at small angular scales. The fine spatial resolution needed to explore these processes, however, lies beyond the capabilities of current astronomical facilities and nearly all proposed future facilities. Interferometers can crack this angular resolution problem, and space-based interferometry missions promise to explore entirely new regions of scientific phase space, providing unique new insights into the physical processes lurking at small angular scales.

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)0.021
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0040.016
Scholarly communication0.0090.025
Open science0.0030.007
Research integrity0.0090.016
Insufficient payload (model declined to judge)0.0130.005

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 head0.006
GPT teacher head0.263
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2019
Admission routes1
Has abstractyes

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Same venueBulletin of the American Astronomical SocietySame topicAstronomy and Astrophysical ResearchFrench-language works237,207