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Record W4384523276 · doi:10.56367/oag-039-10819

Future technology: Multi-purpose plasmas with microwaves

2023· article· en· W4384523276 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueOpen Access Government · 2023
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPlasmaElectronIonMicrowaveAtomic physicsIonizationVolume (thermodynamics)Electric fieldMaterials scienceEngineering physicsPhysicsNuclear physicsThermodynamics

Abstract

fetched live from OpenAlex

Future technology: Multi-purpose plasmas with microwaves Professor Michel Moisan and his team at Université de Montréal (UdeM) explore reliable, energy-efficient and multi-purpose plasmas with microwaves for research and technology. The ability to generate ionized gases (gaseous plasmas) under required operating conditions (shape and volume of the plasma, nature of the gas (including various mixtures), and pressure) is central to many aspects of science and industry. In that respect, of particular interest are low-temperature plasmas where the electrons have high energy while the ions remain slightly above room temperature, allowing, among other things, low-energy chemistry. To achieve reproducible and low-contaminated (electrodeless) gaseous discharges, it is better to call on a high-frequency (HF) electric field: it initially accelerates a few electrons that are randomly present in the gas, which then go on to strip the outer electrons of atoms in a source gas, creating an ‘avalanche’ process, which culminates in a stationary fluid of electrons and ions – a plasma.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.025
GPT teacher head0.299
Teacher spread0.274 · 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