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Record W4238499351 · doi:10.1063/1.3099096

Stellar Spectropolarimetry

2009· article· lt· W4238499351 on OpenAlexaboutno aff
S. C. Marsden, Eric Stempels

Bibliographic record

VenueAIP conference proceedings · 2009
Typearticle
Languagelt
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
Fundersnot available
KeywordsStarsPhysicsTelescopeAstronomySession (web analytics)Magnetic fieldSpectral lineAstrophysicsComputer science

Abstract

fetched live from OpenAlex

Stellar spectropolarimetry involves the study of magnetic fields on stars through the collection of high‐resolution polarised spectra. This field has grown rapidly in recent years with the advent of two new high‐resolution echelle spectropolarimeters available to the astronomical community, ESPaDOnS on the 3.6‐m Canada France Hawaii telescope and NARVAL on the 2‐m Telescope Bernard Lyot. In addition, several groups are now developing new magnetic imaging codes as well as new techniques to recover information from polarised spectra. In light of this a splinter session at Cool Stars XV was organised to present and discuss some of the latest techniques and results in this field. In this paper I attempt to summarise the ideas and results presented at the session.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.020

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 head0.240
Teacher spread0.222 · 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 designBench or experimental
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
Published2009
Admission routes1
Has abstractyes

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