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Record W2990338950

The power of heartbeats through the lens of ι Orionis

2017· book-chapter· en· W2990338950 on OpenAlexfundno aff
H. Pablo, Noel D. Richardson, Jim Fuller, A. F. J. Moffat, Photometry Tiger Team

Bibliographic record

VenueCaltechAUTHORS (California Institute of Technology) · 2017
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersCanadian Space AgencyÖsterreichische ForschungsförderungsgesellschaftUniversität WienUniversity of TorontoKarl-Franzens-Universität GrazFonds Québécois de la Recherche sur la Nature et les TechnologiesMinisterstwo Edukacji i NaukiNatural Sciences and Engineering Research Council of CanadaFundacja na rzecz Nauki Polskiej
KeywordsAsteroseismologyPhysicsStarsStar (game theory)HeartbeatAstrophysicsAmplitudeLens (geology)Power (physics)OpticsComputer scienceComputer security
DOInot available

Abstract

fetched live from OpenAlex

O star asteroseismology is a relatively new field which has not been able to gain significant traction due in large part to the lack of known pulsators in addition to the relatively sparse number of frequencies detected in those pulsators. This is likely due to a combination of factors, chief among them long frequencies, on the order of days, and weak amplitudes (≈ 1 mmag and below). Fortunately, through the discovery of the most massive heartbeat system ι Orionis and it's corresponding tidally induced oscillations with BRITE-Constellation there exists a new avenue with which to explore O star asteroseismology. In this paper we will give a prescription for using tidally induced oscillations to do asteroseismic analysis on O stars and present a list of candidate systems for this analysis within the BRITE sample.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.249
Teacher spread0.227 · 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
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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