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Record W1964255545 · doi:10.1103/physreve.61.3042

Observations of the ultraviolet and x-ray brightness profiles and cooling rates of Kr and Ar in magnetically confined fusion plasmas

2000· article· en· W1964255545 on OpenAlexaff
M. J. May, K. B. Fournier, D. Pacella, H. Kroegler, J. E. Rice, B. C. Gregory, M. Finkenthal, H. W. Moos, G. Mazzitelli, W. H. Goldstein

Bibliographic record

VenuePhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsInstitut National de la Recherche Scientifique
FundersPrinceton Plasma Physics Laboratory
KeywordsAtomic physicsIonizationPlasmaCollisional excitationBrightnessPhysicsTokamakIonNuclear physicsOptics

Abstract

fetched live from OpenAlex

The spatial brightness profiles of Ar and Kr ions have been measured during a set of experiments in which these gases were puffed into FTU (Frascati Tokamak Upgrade) and Alcator C-Mod tokamak plasmas. These profiles were measured by spatially scanning photometrically calibrated vuv and x-ray spectrometers covering 3 to 1700 \AA{} on a shot to shot basis. Several simulations of these profiles were performed using the multiple ionization state transport (MIST) code to validate the atomic physics rates used to determine the charge state distribution in the plasmas. A comparison of two sets of atomic physics rates was made. The chosen rates were the original rates in MIST and the more accurate ionization/recombination rate coefficients from the HULLAC atomic code and the current compilations by Mazzotta. The simulations with the more accurate rates could correctly predict the brightness profiles. The simulations with the older rates adequately predicted the Ar brightness profiles but did not accurately predict those of Kr. The inclusion of the excitation autoionization rates which were absent from the MIST code had the most profound effect on the simulated charge state distributions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.302
Teacher spread0.289 · 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.

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

Citations14
Published2000
Admission routes1
Has abstractyes

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