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Record W2781649930 · doi:10.1126/science.aan0106

An excess of massive stars in the local 30 Doradus starburst

2018· article· en· W2781649930 on OpenAlexaff
F. R. N. Schneider, H. Sana, C. J. Evans, J. M. Bestenlehner, N. Castro, L. Fossati, G. Gräfener, N. Langer, O. H. Ramírez-Agudelo, C. Sabín-Sanjulián, S. Simón‐Díaz, F. Tramper, P. A. Crowther, A. de Koter, S. E. de Mink, P. L. Dufton, M. García, Mark Gieles, V. Hénault-Brunet, A. Herrero, R. G. Izzard, V. M. Kalari, D. J. Lennon, J. Maíz Apellániz, N. Markova, F. Najarro, Philipp Podsiadlowski, J. Puls, W. D. Taylor, Jacco Th. van Loon, J. S. Vink, Colin Norman

Bibliographic record

VenueScience · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsHerzberg Institute of Astrophysics
FundersFonds Wetenschappelijk OnderzoekHorizon 2020 Framework ProgrammeDeutsche ForschungsgemeinschaftEuropean Southern ObservatoryScience and Technology Facilities CouncilEuropean CommissionComisión Nacional de Investigación Científica y TecnológicaHintze Family Charitable Foundation
KeywordsStarsAstrophysicsPhysicsAstronomyAstrobiology

Abstract

fetched live from OpenAlex

The 30 Doradus star-forming region in the Large Magellanic Cloud is a nearby analog of large star-formation events in the distant universe. We determined the recent formation history and the initial mass function (IMF) of massive stars in 30 Doradus on the basis of spectroscopic observations of 247 stars more massive than 15 solar masses ([Formula: see text]). The main episode of massive star formation began about 8 million years (My) ago, and the star-formation rate seems to have declined in the last 1 My. The IMF is densely sampled up to 200 [Formula: see text] and contains 32 ± 12% more stars above 30 [Formula: see text] than predicted by a standard Salpeter IMF. In the mass range of 15 to 200 [Formula: see text], the IMF power-law exponent is [Formula: see text], shallower than the Salpeter value of 2.35.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.446

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 studies0.0000.001
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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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 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

Citations258
Published2018
Admission routes1
Has abstractyes

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