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Record W2316301234 · doi:10.1097/hp.0000000000000505

Particle Density Using Deposition Filters at the Full Scale RDD Experiments

2016· article· en· W2316301234 on OpenAlexaffabout
Rodney Berg, Colleen Gilhuly, Ed Korpach, Kurt Ungar

Bibliographic record

VenueHealth Physics · 2016
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsHealth Canada
Fundersnot available
KeywordsDeposition (geology)PlumeEnvironmental scienceParticle (ecology)Scale (ratio)TRACERComputer scienceNuclear engineeringMaterials sciencePhysicsMeteorologyGeologyNuclear physicsEngineering

Abstract

fetched live from OpenAlex

During the Full-Scale Radiological Dispersal Device (FSRDD) Field Trials carried out in Suffield, Alberta, Canada, several suites of detection equipment and software models were used to measure and characterize the ground deposition. The FSRDD Field Trials were designed to disperse radioactive lanthanum of known activity to better understand such an event. This paper focuses on one means of measuring both concentration and the particle size distribution of the deposition using electrostatic filters placed around the trial site to collect deposited particles for analysis. The measurements made from ground deposition filters provided a basis to guide modeling and validate results by giving insight on how particles are distributed by a plume.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.284
Teacher spread0.248 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2016
Admission routes2
Has abstractyes

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