MétaCan
Menu
Back to cohort
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 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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.002

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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainIncentives
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

Explore more

Same venueInternational Journal of Radiation BiologySame topicRadiation Dose and ImagingFrench-language works237,207