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Record W2592727705 · doi:10.1088/1367-2630/aa639d

Heating neutral beams for ITER: negative ion sources to tune fusion plasmas

2017· article· en· W2592727705 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNew Journal of Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
FundersInstitute of Circulatory and Respiratory Health
KeywordsPhysicsNeutral beam injectionPlasmaFusionBeam (structure)Nuclear engineeringFusion powerIonInjectorPower (physics)Scope (computer science)Nuclear fusionNuclear physicsAtomic physicsTokamakOpticsComputer scienceThermodynamics

Abstract

fetched live from OpenAlex

Neutral beam injection (NBI) based on a negative ion source is one of the basic heating and current drive systems designed for ITER required to reach its goals of the operation with high fusion power, P fus ∼ 500 MW with fusion gain, Q = 10 for 400 s in a baseline scenario, and P fus > 250 MW, Q = 5 operation for 3600 s in an advanced scenario. A total power of 33 MW from the two heating neutral beam (HNB) injectors is envisaged in the present scenario. The scope of the present paper is to provide an overview of the main aspects of the interaction of the HNBs with the ITER plasma. Various operational scenarios with different mixtures of the main ion species, He, H, DD and DT, foreseen at different phases of the ITER operation are considered.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.396

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 communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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