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GrayStarServer: Server-side Spectrum Synthesis with a Browser-based Client-side User Interface

2016· article· en· W2418137391 on OpenAlexafffund

Bibliographic record

VenuePublications of the Astronomical Society of the Pacific · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsSaint Mary's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsASCIIJavaScriptUser interfaceNISTPython (programming language)Asynchronous communicationSource codeByteInterface (matter)Rendering (computer graphics)

Abstract

fetched live from OpenAlex

We present GrayStarServer (GSS), a stellar atmospheric modeling and spectrum synthesis code of pedagogical accuracy that is accessible in any web browser on commonplace computational devices and that runs on a timescale of a few seconds. The addition of spectrum synthesis annotated with line identifications extends the functionality and pedagogical applicability of GSS beyond that of its predecessor, GrayStar3 (GS3). The spectrum synthesis is based on a line list acquired from the NIST atomic spectra database, and the GSS post-processing and user interface client allows the user to inspect the plain text ASCII version of the line list, as well as to apply macroscopic broadening. Unlike GS3, GSS carries out the physical modeling on the server side in Java, and communicates with the JavaScript and HTML client via an asynchronous HTTP request. We also describe other improvements beyond GS3 such as a more physical treatment of background opacity and atmospheric physics, the comparison of key results with those of the Phoenix code, and the use of the HTML element for higher quality plotting and rendering of results. We also present LineListServer, a Java code for converting custom ASCII line lists in NIST format to the byte data type file format required by GSS so that users can prepare their own custom line lists. We propose a standard for marking up and packaging model atmosphere and spectrum synthesis output for data transmission and storage that will facilitate a web-based approach to stellar atmospheric modeling and spectrum synthesis. We describe some pedagogical demonstrations and exercises enabled by easily accessible, on-demand, responsive spectrum synthesis. GSS may serve as a research support tool by providing quick spectroscopic reconnaissance. GSS may be found at www.ap.smu.ca/~ishort/OpenStars/GrayStarServer/grayStarServer.html , and source tarballs for local installations of both GSS and LineListServer may be found at www.ap.smu.ca/~ishort/OpenStars/ .

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)0.002
metaresearch head score (Gemma)0.005
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: Methods · Consensus signal: none
Teacher disagreement score0.101
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0050.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.1010.081

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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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
GenreMethods

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

Citations2
Published2016
Admission routes2
Has abstractyes

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