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Record W1982639476 · doi:10.1017/s1743921308015767

Mass Transfer in Binary Systems: A Numerical Approach

2007· article· en· W1982639476 on OpenAlexafffund
Charles-Philippe Lajoie, Alison Sills

Bibliographic record

VenueProceedings of the International Astronomical Union · 2007
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStarsSmoothed-particle hydrodynamicsBlue stragglerMass transferBinary numberStar (game theory)Boundary (topology)PhysicsAstrophysicsPoint (geometry)Work (physics)MechanicsGeometryMathematicsStar clusterThermodynamics

Abstract

fetched live from OpenAlex

Abstract We present preliminary work on the formation scenario of blue straggler stars by mass transfer in binary systems. More precisely, using Smoothed Particle Hydrodynamics (SPH), we want to model only the outer parts of the stars in order to get a much greater spatial resolution of the mass transfer flow itself. The inner boundary conditions are achieved using the so-called ghost particles and by replacing the inner mass by a central point mass. Stability of this central point mass is crucial, and it is shown that we get reasonable results. These simulations should give us indications on which layers of the donor star are actually transferred to the other star as well as how mass is transferred and how it settles on the accretor. This work is aimed at getting distinct observational signatures which would help identifying the dominant formation mechanism of blue straggler stars.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.277

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 head0.007
GPT teacher head0.200
Teacher spread0.194 · 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 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

Citations0
Published2007
Admission routes2
Has abstractyes

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