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Record W2049229972 · doi:10.1017/s1743921311010908

Probing Be star disks: new insights from Hα spectroscopy and detailed numerical models

2010· article· en· W2049229972 on OpenAlexaff
C. E. Jones, C. Tycner, J. Silaj, A. D. Smith, T. A. A. Sigut

Bibliographic record

VenueProceedings of the International Astronomical Union · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsWestern University
Fundersnot available
KeywordsRadiative transferSpectroscopyStarsAstrophysicsComputational physicsPhysicsSpectral lineStar (game theory)Line (geometry)Spectral resolutionRange (aeronautics)Be starEmission spectrumResolution (logic)Work (physics)OpticsMaterials scienceGeometryThermodynamicsAstronomyComputer scienceMathematics

Abstract

fetched live from OpenAlex

Abstract Hα high resolution spectroscopy combined with detailed numerical models is used to probe the physical conditions, such as density, temperature, and velocity of Be star disks. Models have been constructed for Be stars over a range in spectral types and inclination angles. We find that a variety of line shapes can be obtained by keeping the inclination fixed and changing density alone. This is due to the fact that our models account for disk temperature distributions self-consistently from the requirement of radiative equilibrium. A new analytical tool, called the variability ratio, was developed to identify emission-line stars at particular stages of variability. It is used in this work to quantify changes in the Hα equivalent widths for our observed spectra.

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)0.004
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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