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Record W4389923147 · doi:10.48550/arxiv.2312.09923

Reactive collisions between electrons and BeH+ above dissociation threshold

2023· preprint· en· W4389923147 on OpenAlexfundno aff
E. Djuissi, J. Boffelli, R. Hassaine, Nicolina Pop, V. Laporta, Kalyan Chakrabarti, Mehdi Ayouz, J. Zs. Mezei, I. F. Schneider

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsnot available
FundersNemzeti Kutatási, Fejlesztési és Innovaciós AlapScience and Engineering Research BoardRégion NormandieCentre National de la Recherche ScientifiqueNormandie UniversitéAgence Nationale de la RechercheAgence Universitaire de la FrancophonieEuropean CommissionCentre National d’Etudes SpatialesLabex EMC3
KeywordsDissociation (chemistry)Excited stateAtomic physicsExcitationDissociativeIonDissociative recombinationElectronPhysicsCollisional excitationChemistryIonizationRecombinationNuclear physics

Abstract

fetched live from OpenAlex

Our previous studies of dissociative recombination, and vibrational excitation/de-excitation of the BeH$^+$ ion, based on the multichannel quantum defect theory, are extended to collision energies above the dissociation threshold, taking into account the vibrational continua of the BeH$^+$ ion and, consequently, its dissociative excitation. We have also significantly increased the number of dissociative states of $^2Π$, $^2Σ^+$ and $^2Δ$ symmetry included in our cross section calculations, generating the most excited-ones by using appropriate scaling laws. Our results are suitable for modeling the kinetics of BeH+ in edge fusion plasmas for collision energies up to 12 eV.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.059
GPT teacher head0.206
Teacher spread0.146 · 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 designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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