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Record W3090867349 · doi:10.1051/epjconf/202023904001

A new reference database for beta-delayed neutrons

2020· article· en· W3090867349 on OpenAlexaff
P. Dimitriou, I. Dillmann, Balraj Singh, V.M. Piksaikin, J. L. Taı́n, K. P. Rykaczewski, A. Algora, K. Banerjee, I. N. Borzov, D. Cano‐Ott, Satoshi Chiba, M. Fallot, Daniela Foligno, R. Grzywacz, Xiaolong Huang, T. Marketin, Futoshi Minato, G. Mukherjee, Bertis-Charlie Rasco, A. A. Sonzogni, M. Verpelli

Bibliographic record

VenueEPJ Web of Conferences · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsMcMaster UniversityTRIUMF
Fundersnot available
KeywordsNeutronNuclear dataReference databaseNuclear physicsDatabaseDelayed neutronPhysicsNuclear engineeringComputer scienceNeutron temperatureEngineering

Abstract

fetched live from OpenAlex

A new database containing all available experimental and evaluated β-delayed neutron data is presented in this paper. The database is the product of an international effort coordinated by the International Atomic Energy Agency. It comprises a microscopic section including all available experimental data on beta-decay half-lives, β-delayed neutron emission probabilities and spectra, as well as new systematics and global theoretical calculations for comparison. The beta-delayed neutron data for individual precursors have been benchmarked against available data on macroscopic properties such as total delayed-neutron yields and spectra, delayed-neutron decay curves and time-dependent group parameters using the summation method. All available measured macroscopic data have also been compiled in the macroscopic section of the database. The dedicated database is available online on the IAEA server (URL:http://www-nds.iaea.org/beta-delayed-neutron/database.html).

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.041
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0160.015
Science and technology studies0.0010.000
Scholarly communication0.0050.006
Open science0.0060.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0410.042

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 head0.053
GPT teacher head0.290
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations2
Published2020
Admission routes1
Has abstractyes

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