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Record W2072946931 · doi:10.1093/rpd/ncq203

Report of the workshop on biological dosimetry: increasing capacity for emergency response

2010· article· en· W2072946931 on OpenAlexafffundabout
Vinita Chauhan, Ruth C. Wilkins

Bibliographic record

VenueRadiation Protection Dosimetry · 2010
Typearticle
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsHealth Canada
FundersCanadian Armed ForcesDefence Research and Development Canada
KeywordsDosimetryRadiological weaponTriagePreparednessMedical physicsBiological warfareRisk analysis (engineering)MedicineBusinessComputer scienceMedical emergencyPolitical scienceNuclear medicine

Abstract

fetched live from OpenAlex

Recent events have brought increased attention to the possibility and dangers of a radiological terrorist threat and its potential implication on the national capacity for radiation accident preparedness. In such an event, there is a pressing need to rapidly identify severely irradiated individuals who require prompt medical attention from those who have not been exposed or have been subject to low doses. Initial dose assessment is a key component in rapid triage and treatment, however, the development of accurate methods for rapid dose assessment remains a challenge. In this report, the authors describe a recent workshop supported by the Chemical, Biological, Radiological-Nuclear and Explosives Research and Technology Initiative regarding the international effort to increase biological dosimetry capacity to effectively mount an emergency response in a mass casualty situation. Specifically, the focus of the workshop was on the current state of biological dosimetry capabilities and capacities in North America, recent developments towards increasing throughput for biological dosimetry and to identify opportunities for developing a North American Biological Dosimetry Network and forming partnerships and collaborations within Canada and the USA.

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)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.302
Teacher spread0.273 · 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.

Study designBench or experimental
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

Citations2
Published2010
Admission routes3
Has abstractyes

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