MétaCan
Menu
← Back to cohort
Record W2800662092 · doi:10.3390/en11040988

X-Pinch Plasma Generation Testing for Neutron Source Development and Nuclear Fusion

2018· article· en· W2800662092 on OpenAlexaffabout
Hossam A. Gabbar, C.A. Barry Stoute, Daniel Bondarenko, Nicholas Tarsitano, Anas Abdel Rihem, Stefan Sirakov, Shraddhey Jani, Samskruthi Menashi

Bibliographic record

VenueEnergies · 2018
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsNuclear fusionNuclear engineeringPlasmaFusion powerZ-pinchPinchNuclear physicsFusionNeutronNuclear dataPhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Nuclear fusion is a sought-out technology in which two light elements are fused together to create a heavier element and releases energy. Two primary nuclear fusion technologies are being researched today: magnetic and inertial confinement. However, a new type of nuclear fusion technology is currently being research: multi-pinch plasma beams. At the University of Ontario Institute of Technology, there is research on multi-pinch plasma beam technology as an alternative to nuclear fusion. The objective is to intersect two plasma arcs at the center of the chamber. This is a precursor of nuclear fusion using multi-pinch. The innovation portion of the students’ work is the miniaturization of this concept using high energy electrical DC pulses. The experiment achieved the temperature of 2300 K at the intersection. In comparison to the simulation data, the temperature from the simulation is 7000 K at the intersection. Additionally, energy harvesting devices, both photovoltaics and a thermoelectric generator, were placed in the chamber to observe the viable energy extraction.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.024
GPT teacher head0.215
Teacher spread0.191 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueEnergies→Same topicParticle accelerators and beam dynamics→French-language works237,207→