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Record W2969811330 · doi:10.5055/jem.2019.0425

Nuclear emergencies and natural disasters

2019· article· en· W2969811330 on OpenAlexaff
Jean-Francois Lafortune, Edward Waller

Bibliographic record

VenueJournal of Emergency Management · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsNatural disasterRadiological weaponNuclear power plantNuclear powerPlan (archaeology)Nuclear disasterNatural (archaeology)Emergency managementEnvironmental planningEmergency responseAccident (philosophy)Risk analysis (engineering)BusinessNuclear plantMedical emergencyEngineeringGeographyPolitical scienceMedicineNuclear engineeringMeteorology

Abstract

fetched live from OpenAlex

The Fukushima disaster following the March 11, 2011 earthquake and tsunami in Japan demonstrates the complexity of responding to nuclear emergencies during a natural disaster. Current international safety standards and guidance do not specifically address this type of situation. The potential conflicts between the response to the conventional impacts and the radiological consequences, real and perceived, can impede the effectiveness of the overall emergency response. The present article discusses the strategic and operational challenges likely to be encountered in such a complex emergency, and draws conclusions on how countries should better plan for the low probability but high consequence impacts of natural disasters coincident with a nuclear accident at a nuclear power plant.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.303
Teacher spread0.288 · 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 designNot applicable
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

Citations1
Published2019
Admission routes1
Has abstractyes

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