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Record W2132351120 · doi:10.1017/s1743921313013331

SPICES: A Mission Concept to Characterize Long Period Planets from Giants to Super-Earths

2012· article· en· W2132351120 on OpenAlexaff
A. Boccaletti, A.-L. Maire, R. Galicher, Pierre Baudoz, Dimitri Mawet, John T. Trauger, Jean Schneider, W. Traub, D. M. Stam, Pierre-Olivier Lagage, R. Gratton

Bibliographic record

VenueProceedings of the International Astronomical Union · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsNational Research Council CanadaHerzberg Institute of Astrophysics
Fundersnot available
KeywordsZodiacal lightExoplanetSolar SystemAstronomyPhysicsPlanetContext (archaeology)AstrobiologyStarsJupiter (rocket family)Planetary systemTelescopeRemote sensingGeologySpace exploration

Abstract

fetched live from OpenAlex

Abstract SPICES (Spectro-Polarimetric Imaging and Characterization of Exoplanetary Systems) was proposed in 2010 for a five-year M-class mission in the context of ESA Cosmic Vision. Its purpose is to image and characterize long-period extrasolar planets located at several AUs (0.5-10 AU) from nearby stars (<25 pc) with masses ranging from a few Jupiter masses down to super-Earths (~2 Earth radii, ~10 M⊕), possibly habitable. In addition, circumstellar disks as faint as a few times the zodiacal light in the Solar System can be studied. SPICES is based on a 1.5-m off-axis telescope and can perform spectro-polarimetric measurements in the visible (450 - 900 nm) at a spectral resolution of about 40. This paper summarizes the top science program and the choices made to conceive the instrument. The performance is illustrated for a few emblematic cases.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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 designSimulation or modeling
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
Published2012
Admission routes1
Has abstractyes

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