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Record W7028221469

Electron impact excitation studies of laser-excited and ground-state barium and ytterbium

2015· dissertation· en· W7028221469 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2015
Typedissertation
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsExcited stateBariumElectron ionizationGround stateExcitationYtterbiumMetastabilityEmission spectrumElectronScattering
DOInot available

Abstract

fetched live from OpenAlex

The research presented in this dissertation was performed in the Atomic, Molecular and Optical (AMO) physics laboratory at the University of Manitoba. Atomic beams of the two-valence-electron heavy atom systems, barium and ytterbium, were investigated with low energy electron scattering and optical emission studies. Both the ground states and laser excited states were investigated as a function of incident electron beam energy from 10 eV to 50 eV. Measurements of relative cross sections and polarization for 583 nm and 554 nm line emission from the (6s7p)1P1 and (6s6p)1P1 states of barium excited by electron impact from both the ground states and the optically pumped metastable (6s5d)1D2 are reported. Data are normalized to absolute cross sections for the ground state (6s2)1S0→(6s6p)1P1 state transition due to electron scattering, with corrections for branching ratios and cascading from higher states to deduce the total level excitation cross sections. Results are also presented for the first studies of the 399 nm line emission from laser-excited ytterbium, yielding an upper limit on the apparent cross section for the (6s6p)3P1→(6s6p)1P1 transition. Results are compared with the latest theoretical models and previous data, where available.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.250
Teacher spread0.219 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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