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Record W2084014986 · doi:10.1051/0004-6361:20041358

Atomic data from the IRON Project

2005· article· en· W2084014986 on OpenAlexfundno aff
M. C. Chidichimo, G. Del Zanna, H. E. Mason, N. R. Badnell, J. A. Tully, K A Berrington

Bibliographic record

VenueAstronomy and Astrophysics · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsnot available
FundersDivision of Mathematical SciencesNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsAtomic physicsBerylliumExcited stateCollisionIonizationR-matrixElectronIonRange (aeronautics)Electron ionizationMatrix (chemical analysis)Wave functionGround stateNuclear physicsQuantum mechanicsChemistry

Abstract

fetched live from OpenAlex

Collision strengths for electron induced transitions in the beryllium-like ion Fe+22 are calculated using the intermediate coupling frame transformation (ICFT) version of the R-matrix programs. Our target has 98 fine structure states corresponding to and . The present calculation is for electron impact energies in the range 3.15 to 380 Ry. When T exceeds about ten million degrees one needs to take account of contributions to the thermally averaged collision strength coming from electrons with energies in excess of 380 Ry. We discuss a way of allowing for these contributions. Values of for all the transitions between the ground state and the excited states , with are tabulated as a function of . The temperature range is centred on which is approximately where Fe+22 has maximum abundance in ionization equilibrium. To the best of our knowledge these are the first R-matrix calculations for Fe+22 for excitations to the levels. Good agreement with previous distored-wave calculations is found. However, the resonance contributions have an important effect on the effective collision strengths and in turn on the level populations.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.243
Teacher spread0.227 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations28
Published2005
Admission routes1
Has abstractyes

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