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Industry Bibliographical Databases: Perspectives of Use in the FMBA of Russia for Scientific Expertise in Decision-Making. Report 1. General Issues and Database on Health and Other Effects in Nuclear Workers

2025· article· en· W4408908106 on OpenAlexaboutno aff
А. Н. Котеров, Л.Н. Ушенкова, T.M. Bulanova, N.A. Bogdanenko

Bibliographic record

VenueMedical Radiology and radiation safety · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicNuclear Issues and Defense
Canadian institutionsnot available
Fundersnot available
KeywordsDatabaseNational databasePolitical scienceComputer science

Abstract

fetched live from OpenAlex

The presented review of three reports is devoted to bibliographic databases on health and other effects and indexes in nuclear workers (NW) and uranium miners (U miners), developed within the framework of the research theme of the Federal Medical and Biological Agency of Russia (FMBA) and registered with the state in Rospatent. Report 1 sets out introductory issues of the theory of databases, as well as registers, and provides detailed information on the database for NW. The purpose of the database for NW creating was to form a repository for accessible for abstract and full-text search published data on themes relevant for conducting research examinations for expertise in the system of the FMBA, in other healthcare institutions dealing with the radiation factor, and, more broadly, for conducting fundamental and applied research in the field of professional exposures. The main parts of the database are two separate sub-databases for Russian and foreign NW (Russian NW and Foreign NW), in which the sources are collected in alphabetical order by the authors of the publication or the organizations that created the document. The structural form of information is a catalog that includes primary (main) units of information in the form of an information file about the source (DOC), which contains the title of the publication/document, an abstract (sometimes additional information), and the full original publication (PDF, rarely HTML), available for 88–91 % of sources (the Russian and Foreign sub-databases contain 2078 and 2145 sources, respectively, as of the end of January 2025). 51 % of the works in the database correspond to studies for Russian NW; followed by the USA, Great Britain, Canada, France, and Japan. Visual and/or software search of the material in the database it is supposed to be carried out both through the information names of the catalogs, including the themes of research, carried out using the list of abbreviations (metadata for the database), and through all the texts of the sources included in the database using the proposed programs. Auxiliary elements of the database are fragments of two sub-bases that have undergone hierarchical thematic cataloging in accordance with the identified areas of research on the effects and indexes for NW. These elements are intended, firstly, for initial familiarization with the subject of the database for NW, and, secondly, they are significant as a final thematic base with a certain number of sources, which can be used directly for operational purposes. The developed database for NW has no analogues among industry databases/registers for NW in various countries, nor among bibliographic and search systems. PubMed, Cochrane Library, EMBASE, CINAHL, ISRCTN, Web of Science and Google revealed 5–24 times fewer sources on the theme than the proposed database, and in most cases the world search systems do not provide for the extraction of original publications (as for the IAEA INIS bibliographic database on radiation effects). The depth of the search for works on effects and indexes for NW in the world systems is significantly inferior to the developed database (1960–1970s versus 1940–1950s). It is concluded that the presented database on NW is unique for examination within the framework of the FMBA and other healthcare institutions, and has no complete replacement as a scientific reference and expert depot of sources.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.387
Teacher spread0.361 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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