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Record W2031547674 · doi:10.1109/icpadm.2006.284260

Unifying Representation of Collision Cross Sections in Gases

2006· article· en· W2031547674 on OpenAlexaff
G. R. Govinda Raju

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMomentum transferCross section (physics)Atomic physicsPolarRange (aeronautics)IonizationMomentum (technical analysis)CollisionChemical polarityMoleculeScatteringSemiconductorChemistryPhysicsMaterials scienceOptoelectronicsComputer scienceIonOptics

Abstract

fetched live from OpenAlex

Electrical discharges in gases continues to be an active area of research for industrial applications such as power systems, environmental clean up, laser technology, semiconductor fabrication etc. A fundamental knowledge of electron-gas-neutral interaction is indispensable and, of the various types of cross sections, the more important ones are the momentum transfer and ionization cross sections. In this paper the energy dependence of the momentum transfer cross sections are categorized according to the nature of the species of the neutral. The species are (1) rare gas atoms (2) di-atomic molecules with combinations of polar, non-polar, attaching, and non-attaching properties (3) poly-atomic molecules with combinations of polar, non-polar, attaching, and non-attaching properties. New results are provided for momentum transfer for selected gases by analyzing the data of scattering cross sections. Conclusions are drawn high lighting the energy range in which the momentum transfer cross section data are required

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.268
Teacher spread0.255 · 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 designTheoretical or conceptual
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

Citations0
Published2006
Admission routes1
Has abstractyes

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