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Record W4408053283 · doi:10.1088/1741-4326/adb8fb

Thermal energy confinement time of spherical tokamak plasmas in PI3

2025· article· en· W4408053283 on OpenAlexaff
A. Tancetti, C. Ribeiro, Stephen Howard, Simon Coop, Colin P. McNally, M. W. Reynolds, П.П. Холодов, F. G. Braglia, Ryan Zindler, Carolyn A. MacDonald, Edward Love, Patrick Carle, X. Feng, A. Rohollahi, K. Leci, David Plant, Carl Dunlea, R.S. Ivanov, Alex Mossman

Bibliographic record

VenueNuclear Fusion · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsGeneral Fusion (Canada)
Fundersnot available
KeywordsSpherical tokamakTokamakPlasmaThermalMagnetic confinement fusionPlasma confinementEnergy (signal processing)PhysicsMaterials scienceAtomic physicsNuclear engineeringNuclear physicsMeteorology

Abstract

fetched live from OpenAlex

Abstract The Plasma Injector 3 (PI3) experiment at general fusion has been constructed to demonstrate the ability to form plasma targets suitable for compression in a Magnetized Target Fusion machine. To achieve compressive heating to fusion conditions, the target plasmas should have an energy confinement time sufficiently in excess of the compression time. In this work we present a methodology for calculating this timescale and present results for a large set of discharges. Characterization of the plasma current profiles reveals trends and groupings determined by machine settings. The largest energy confinement times have been obtained for discharges with a broad plasma current profile, fresh lithium coating on the device walls, and a near constant toroidal field. We find that PI3 plasmas at 5 ms into the discharge can have thermal confinement times in excess of 10 ms . These meter-scale plasma can thus achieve significant heating if compressed on a timescale of milliseconds.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.005
GPT teacher head0.225
Teacher spread0.220 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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