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Exercise Maritime Response (EXMR): Lessons Learned for Population Monitoring and Communications

2007· article· en· W2029987409 on OpenAlexaffabout
Gary H. Kramer, Sonia Johnson, Barry M. Hauck, Kevin Capello, Debora Quayle

Bibliographic record

VenueHealth Physics · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsHealth Canada
Fundersnot available
KeywordsEmergency responseTerrorismPopulationRadiological weaponAeronauticsRadioactive contaminationTest (biology)Medical emergencyEnvironmental healthEngineeringOperations managementPublic relationsBusinessMedicinePolitical scienceContamination

Abstract

fetched live from OpenAlex

Exercise Maritime Response was the third in a series of four emergency response exercises sponsored by the Chemical, Biological, Radiological and Nuclear Research and Technology Initiative. It was designed to test the Canadian Federal, Provincial and Municipal response to a terrorist attack using radioactive materials. The complexity of this exercise had been increased over previous exercises to now include simulated contaminated members of the public. This paper summarizes the experiences, and the lessons learned, of the Health Canada (HC) team. The largest issues identified by the HC team were: crowd control, insufficiency of staff to deal with surge capacity, and communications. The exercise did prove that the population monitoring equipment worked well and that small amounts of radioactivity were easily identified and quantified to within 20% of their true value.

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)0.010
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0040.004
Open science0.0040.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.081
GPT teacher head0.391
Teacher spread0.310 · 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 designObservational
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

Citations3
Published2007
Admission routes2
Has abstractyes

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