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Record W2152784817 · doi:10.2478/s13540-014-0205-1

FCAA related news, events and books (FCAA-Volume 17-4-2014)

2014· article· en· W2152784817 on OpenAlexfundno aff
Virginia Kiryakova

Bibliographic record

VenueFractional Calculus and Applied Analysis · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
FundersRussian Academy of SciencesCairo UniversityLeverhulme TrustUniversity of AlbertaUniversity of South FloridaImperial College LondonCity University of Hong Kong
KeywordsMathematicsVolume (thermodynamics)ThermodynamicsPhysics

Abstract

fetched live from OpenAlex

This is a Special Issue partly dedicated to the 80th Anniversary of Professor Ivan Dimovski with invited papers and some selected papers from TMSF '14 ---1.Great success for the FCAA Journal In the end of July 2014, Web of Knowledge of Thomson Reuters and Scopus of Elsevier announced the scientific impact metrics for 2013.FCAA have been indexed there since 2011 and it is the first time it receives these metrics officially.According to Thomson Reuters' 2013 Journal Citation Reports (JCR) Science Edition, Release July 2014, http://admin-apps.webofknowledge.com/JCR/JCR, the Journal Title "FRACT CALC APPL ANAL" has Impact

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.650
Threshold uncertainty score0.927

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.009
Science and technology studies0.0030.001
Scholarly communication0.0130.003
Open science0.0020.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.3500.210

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 head0.013
GPT teacher head0.192
Teacher spread0.179 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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