MétaCan
Menu
← Back to cohort
Record W3035004319 · doi:10.1093/mnras/staa3124

<i>Chandra</i> spectral measurements of the O supergiant ζ Puppis indicate a surprising increase in the wind mass-loss rate over 18 yr

2020· article· en· W3035004319 on OpenAlexfundno aff
David H. Cohen, Jiaming Wang, V. Pétit, Maurice A. Leutenegger, Lamiaa Dakir, Chloe Mayhue, Alexandre David-Uraz

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaSwarthmore CollegeNational Aeronautics and Space Administration
KeywordsPhysicsSupergiantAstrophysicsAstronomyStellar windBlue supergiantStarsRed supergiant

Abstract

fetched live from OpenAlex

ABSTRACT New long Chandra grating observations of the O supergiant ζ Pup show not only a brightening of the X-ray emission line flux of 13 per cent in the 18 yr since Chandra’s first observing cycle, but also clear evidence – at more than 4σ significance – of increased wind absorption signatures in its Doppler-broadened line profiles. We demonstrate this with non-parametric analysis of the profiles as well as Gaussian fitting and then use line-profile model fitting to derive a mass-loss rate of 2.47 ± 0.09 × 10−6${\mathrm{M_{\odot }~{\mathrm{y}r^{-1}}}}$, which is a 40 per cent increase over the value obtained from the cycle 1 data. The increase in the individual emission line fluxes is greater for short-wavelength lines than long-wavelength lines, as would be expected if a uniform increase in line emission is accompanied by an increase in the wavelength-dependent absorption by the cold wind in which the shock-heated plasma is embedded.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.211
Teacher spread0.193 · 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 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

Citations9
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMonthly Notices of the Royal Astronomical Society→Same topicStellar, planetary, and galactic studies→French-language works237,207→