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Record W2781996299 · doi:10.3847/1538-4357/aa9c82

The Solar-flux Third Granulation Signature

2018· article· en· W2781996299 on OpenAlexaff
David F. Gray, Benjamín Oostra

Bibliographic record

VenueThe Astrophysical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsPhysicsAstrophysicsSpectrographFlux (metallurgy)Spectral resolutionLine (geometry)WavelengthSpectral lineAstronomyOpticsGeometry

Abstract

fetched live from OpenAlex

Abstract The velocity shifts of spectral lines as a function of line strength, so-called the third signature of granulation, are investigated using three published solar-flux atlases. We use flux atlases because we wish to treat the Sun as a star, against which stellar observations can be compared and judged. The atlases are critiqued and compared to the lower-resolution observations taken with the Elginfield stellar spectrograph. Third-signature plots are constructed for the 6020–6340 Å region. No dependence on excitation potential or wavelength is found over this wavelength span. The shape of the plots from the three solar atlases is essentially the same, with rms line-core velocity differences of 30–35 m s −1 . High-resolution atlas data are degraded to the level of the Elginfield spectrograph and compared to direct observations taken with that spectrograph. The line-core velocities show good agreement, with rms differences of 38 m s −1 . A new standard curve is derived and compared with the previously published one. Only small differences in shape are found, but a significant (+97 m s −1 ) change in the zero point is indicated. The bisector of the Fe i 6253 line is mapped onto the third-signature plots and flux deficits are derived, which measure the granule/lane flux imbalance. The lower spectral resolution lowers the flux deficit area slightly and moves the peak of the deficit 0.3–0.5 km s −1 toward higher velocities. These differences, while significant, are not large compared to measurement errors for stellar 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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.223
Teacher spread0.218 · 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.

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

Citations16
Published2018
Admission routes1
Has abstractyes

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