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Record W2165743999 · doi:10.1017/s1743921307000245

Observing convection in stellar atmospheres

2006· article· en· W2165743999 on OpenAlexfundno aff
J. D. Landstreet

Bibliographic record

VenueProceedings of the International Astronomical Union · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStarsPhysicsAstrophysicsLine (geometry)ConvectionSpectral lineCurvatureStellar atmosphereField (mathematics)Atmosphere (unit)ThermalVector fieldComputational physicsAstronomyMechanicsGeometryMeteorology

Abstract

fetched live from OpenAlex

Abstract Convection occurs in the visible photospheric layers of most stars having T e less than about 10000 K, and in some hotter stars. The solar granulation pattern is a symptom of this, as is the non-zero microturbulent velocity often required in abundance analysis to make both weak and strong lines yield the same abundance. In very sharp-lined stars, the presence of a non-thermal velocity field in the visible stellar atmosphere leads to several other effects which may be detected in spectral line profiles. These include radial velocities that vary systematically with equivalent width, distortions of the line profile as compared to a profile computed with a Voigt profile and rotational broadening (“macroturbulence”), and asymmetries with respect to the line centre (“bisector curvature”). Detection and interpretation of these effects, with the goal of obtaining empirical information about a velocity field present in the visible layers, requires comparison with calculated synthetic spectra which incorporate model velocity fields. Thus, this review will summarize some of the observational clues concerning photospheric velocity fields, as well as modelling aimed at interpreting these data.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.198
Teacher spread0.191 · 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 teacher head, 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

Citations4
Published2006
Admission routes1
Has abstractyes

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