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Record W2518072455 · doi:10.1002/ppap.201600098

LXCat: an Open‐Access, Web‐Based Platform for Data Needed for Modeling Low Temperature Plasmas

2016· article· en· W2518072455 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePlasma Processes and Polymers · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsYork UniversityUniversity of British Columbia
FundersFundação para a Ciência e a TecnologiaAustralian Research CouncilTechnische Universiteit EindhovenCentre National de la Recherche ScientifiqueScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaBranco Weiss Fellowship – Society in ScienceInternational Atomic Energy AgencyBoard of Research in Nuclear SciencesNational Science Foundation
KeywordsPlasmaSwarm behaviourIonScatteringComputer scienceOpen sourceMaterials scienceDatabaseWorld Wide WebPhysicsOperating systemNuclear physicsOpticsArtificial intelligence

Abstract

fetched live from OpenAlex

LXCat is an open‐access platform ( www.lxcat.net ) for curating data needed for modeling the electron and ion components of technological plasmas. The data types presently supported on LXCat are scattering cross sections and swarm/transport parameters, ion‐neutral interaction potentials, and optical oscillator strengths. Twenty‐four databases contributed by different groups around the world can be accessed on LXCat. New contributors are welcome; the database contributors retain ownership and are responsible for the contents and maintenance of the individual databases. This article summarizes the present status of the project.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.783

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 studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.054
GPT teacher head0.323
Teacher spread0.269 · 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