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Record W2907017196 · doi:10.1080/09553002.2018.1558303

Funding for radiation research: past, present and future

2019· review· en· W2907017196 on OpenAlexafffundabout
Tatsuhiko Imaoka, Dmitry Klokov, Tatjana Paunesku, Sisko Salomaa, Mandy Birschwilks, Simon Bouffler, Antone L. Brooks, Tom K. Hei, Toshiyasu Iwasaki, Tetsuya Ono, Kazuo Sakai, Andrzej Wójcik, Gayle E. Woloschak, Yutaka Yamada, Nobuyuki Hamada

Bibliographic record

VenueInternational Journal of Radiation Biology · 2019
Typereview
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsCanadian Nuclear LaboratoriesUniversity of Ottawa
FundersCongressionally Directed Medical Research ProgramsAtomic Energy of Canada LimitedNational Cancer InstituteNational Institutes of HealthGovernment of CanadaU.S. Department of EnergyAustralian GovernmentBruce PowerNational Aeronautics and Space Administration
KeywordsMultidisciplinary approachEuropean unionPolitical scienceIonizing radiationBusinessEngineering ethicsPublic relationsEngineeringEconomic policyPhysics

Abstract

fetched live from OpenAlex

For more than a century, ionizing radiation has been indispensable mainly in medicine and industry. Radiation research is a multidisciplinary field that investigates radiation effects. Radiation research was very active in the mid- to late 20th century, but has then faced challenges, during which time funding has fluctuated widely. Here we review historical changes in funding situations in the field of radiation research, particularly in Canada, European Union countries, Japan, South Korea, and the US. We also provide a brief overview of the current situations in education and training in this field. A better understanding of the biological consequences of radiation exposure is becoming more important with increasing public concerns on radiation risks and other radiation literacy. Continued funding for radiation research is needed, and education and training in this field are also important.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.001
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 head0.207
GPT teacher head0.506
Teacher spread0.299 · 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
GenreReview

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

Citations23
Published2019
Admission routes3
Has abstractyes

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