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Record W3090079247 · doi:10.1051/epjconf/202023911004

Resonance evaluation of Gadolinium isotopes

2020· article· en· W3090079247 on OpenAlexaff
Luiz Leal, Vladimir Sobes, Federico Rocchi, Danila Roubtsov, Jimmy Chow, C. Massimi

Bibliographic record

VenueEPJ Web of Conferences · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsGadoliniumIsotopeResonance (particle physics)PhysicsFormalism (music)Nuclear magnetic resonanceNuclear medicineNuclear physicsChemistryMedicineAtomic physics

Abstract

fetched live from OpenAlex

The objective of this paper is to present the results of an evaluation of the gadolinium isotopes with the main focus on the isotopes155Gd and157Gd. The evaluations were carried out in the resolved resonance region using the Reich-Moore formalism. The originality on the155Gd and157Gd evaluations is the addition of new high-resolution capture cross section measurements performed at the neutron time-of-flight, n_TOF facility for enriched samples and the statistical analysis of the resonance parameters. The resonance analysis was performed with the multilevel R-matrix code SAMMY together with the generalized least-squares technique based on the Bayes’ theory.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.056
GPT teacher head0.303
Teacher spread0.247 · 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
Published2020
Admission routes1
Has abstractyes

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