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Record W2127556270 · doi:10.1086/428929

New Solar Opacities, Abundances, Helioseismology, and Neutrino Fluxes

2005· article· en· W2127556270 on OpenAlexaboutno aff
John N. Bahcall, Aldo Serenelli, Sarbani Basu

Bibliographic record

VenueThe Astrophysical Journal · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsnot available
Fundersnot available
KeywordsHelioseismologyOpacityPhysicsNeutrinoRadiative transferStandard solar modelSolar neutrinoAstrophysicsConvection zoneFlux (metallurgy)Radiative fluxAbundance of the chemical elementsHeliumNuclear physicsOpticsNeutrino oscillationChemistryStarsAtomic physics

Abstract

fetched live from OpenAlex

We construct solar models with the newly calculated radiative opacities from the Opacity Project (OP) and with recently determined (lower) heavy-element abundances. We compare the results from the new models with the predictions of a series of models that use OPAL radiative opacities, older determinations of the surface heavy-element abundances, and refinements of nuclear reaction rates. For all the variations we consider, solar models that are constructed with the newer and lower heavy-element abundances advocated by Asplund et al. disagree by much more than the estimated measuring errors with the helioseismological determinations of the depth of the solar convective zone, the surface helium composition, the internal sound speeds, and the density profile. Using the new OP radiative opacities, the ratio of the 8 B neutrino flux calculated with the older and larger heavy-element abundances (or with the newer and lower heavy-element abundances) to the total neutrino flux measured by the Sudbury Neutrino Observatory is 1.09 (0.87) with a 9% experimental uncertainty and a 16% theoretical uncertainty, 1 σ errors.

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)0.003
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 score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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

Citations633
Published2005
Admission routes1
Has abstractyes

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