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Record W4387455041 · doi:10.3847/1538-3881/acf3e9

Centimeter-sized Grains in the Compact Dust Ring around Very-low-mass Star CIDA 1

2023· article· en· W4387455041 on OpenAlexaff
Jun Hashimoto, Hauyu Baobab Liu, Ruobing Dong, Beibei Liu, Takayuki Muto, Yuka Terada

Bibliographic record

VenueThe Astronomical Journal · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of Victoria
FundersNational Astronomical Observatory of JapanNational Institutes of Natural SciencesJapan Society for the Promotion of ScienceEuropean Space AgencyNational Science FoundationKorea Astronomy and Space Science InstituteNational Radio Astronomy Observatory
KeywordsPhysicsAlgorithmComputer science

Abstract

fetched live from OpenAlex

Abstract We examined the grain size in the dust ring encircling the 0.19 M ☉ T Tauri star CIDA 1 using the Karl G. Jansky Very Large Array at multiple centimeter wavelengths, with a spatial resolution of 0.″2–0.″9. We detected distinct partial-ring structures at these wavelengths around CIDA 1. Based on spatial distributions and spectral indices, we determined that these centimeter emissions originated from dust, rather than free–free or synchrotron emissions. To estimate the maximum grain size ( a max ) within the ring, we compared the observed spectral energy distribution (SED) with SEDs calculated for different a max values using radiative transfer calculations. Our findings indicate an a max value of approximately 2.5 cm in the ring, assuming that the dust opacity can be approximated by the DSHARP models. These results suggest that grain growth took place within the CIDA 1 ring, potentially facilitating more efficient planet formation through pebble accretion scenarios involving centimeter-sized pebbles.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.257
Teacher spread0.237 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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