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Record W2032866683 · doi:10.1115/1.4026390

Experimental Modeling of Wind-Driven Bin-by-Bin Resuspension Factors of Freshly Fallen Radionuclides After an Energetic Release From a Radiological Dispersal Device

2014· article· en· W2032866683 on OpenAlexaff
Sharman Perera, Edward Waller

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBinRadionuclideEnvironmental scienceParticle (ecology)Range (aeronautics)Wind tunnelTRACERAtmospheric sciencesHydrology (agriculture)Nuclear physicsPhysicsMaterials scienceGeotechnical engineeringMechanicsGeologyOceanographyEngineering

Abstract

fetched live from OpenAlex

Small-scale experiments were carried out to characterize the resuspension factor of radioactive lanthanum oxide powder in an environmentally controlled wind tunnel, with the majority using particle sizes less than 10 μm in order to assess the impact of wind resuspension stresses and surface roughness conditions on resuspension. Operational principles of the measuring devices used in the radionuclide resuspension experiments and corresponding uncertainties are discussed. The average bin-by-bin particle resuspension factors (ki) for particle sizes, in the range of 0.25–7.00 μm and 7.00–12.5 μm for downwind fallout locations, were calculated and are reported here as 1.14×10−3 1/m and 4.39×10−2 1/m, respectively.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.292
Teacher spread0.264 · 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 designSimulation or modeling
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
Published2014
Admission routes1
Has abstractyes

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