MétaCan
Menu
Back to cohort
Record W2809478146 · doi:10.1088/1361-6595/aace1b

Influence of gas flow on the performance of surface-wave discharges sustained in capillary tubes

2018· article· en· W2809478146 on OpenAlexaff
J. A. Ruiz Martínez, Eduardo Castaños-Martínez, Cristina González-Gago, Rocío Rincón, María Dolores Calzada, J. Muñoz

Bibliographic record

VenuePlasma Sources Science and Technology · 2018
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité de Montréal
FundersUniversidad de Córdoba
KeywordsCapillary actionFlow (mathematics)MechanicsSurface (topology)Materials scienceChemistryAnalytical Chemistry (journal)Composite materialChromatographyPhysicsGeometryMathematics

Abstract

fetched live from OpenAlex

Abstract The performance of argon surface-wave plasma columns generated at atmospheric pressure in capillary tubes has been tested under a wide range of total gas flow conditions, with special attention to column length and gas temperature as key parameters for the application of such discharges in material synthesis and/or modification and chemical analysis. Varying the total gas flow allows significant control over the total column length and gas temperature, thus modulating both the residence time and the efficiency of chemical processes taking place within the discharge. Moreover, an important connection between the physical mechanisms responsible for discharge sustainment in SWDs and gas flow has been experimentally proven.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.010
GPT teacher head0.227
Teacher spread0.218 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

Explore more

Same venuePlasma Sources Science and TechnologySame topicPlasma Applications and DiagnosticsFrench-language works237,207