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Record W7113449960

Wolf-Rayet Stars

2015· other· en· W7113449960 on OpenAlexaboutno aff

Bibliographic record

Venuepublish.UP (University of Potsdam) · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGeorge (robot)StarsFace (sociological concept)Subject (documents)
DOInot available

Abstract

fetched live from OpenAlex

Nearly 150 years ago, the French astronomers Charles Wolf and Georges Rayet described stars with very conspicuous spectra that are dominated by bright and broad emission lines. Meanwhile termed Wolf-Rayet Stars after their discoverers, those objects turned out to represent important stages in the life of massive stars. As the first conference in a long time that was specifically dedicated to Wolf-Rayet stars, an international workshop was held in Potsdam, Germany, from 1.-5. June 2015. About 100 participants, comprising most of the leading experts in the field as well as as many young scientists, gathered for one week of extensive scientific exchange and discussions. Considerable progress has been reported throughout, e.g. on finding such stars, modeling and analyzing their spectra, understanding their evolutionary context, and studying their circumstellar nebulae. While some major questions regarding Wolf-Rayet stars still remain open 150 years after their discovery, it is clear today that these objects are not just interesting stars as such, but also keystones in the evolution of galaxies. These proceedings summarize the talks and posters presented at the Potsdam Wolf-Rayet workshop. Moreover, they also include the questions, comments, and discussions emerging after each talk, thereby giving a rare overview not only about the research, but also about the current debates and unknowns in the field. The Scientific Organizing Committee (SOC) included Alceste Bonanos (Athens), Paul Crowther (Sheffield), John Eldridge (Auckland), Wolf-Rainer Hamann (Potsdam, Chair), John Hillier (Pittsburgh), Claus Leitherer (Baltimore), Philip Massey (Flagstaff), George Meynet (Geneva), Tony Moffat (Montreal), Nicole St-Louis (Montreal), and Dany Vanbeveren (Brussels).

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.031
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0000.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0310.031

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 head0.021
GPT teacher head0.203
Teacher spread0.182 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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