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Record W1972190784 · doi:10.4031/mtsj.43.4.10

Site Assessment and Risk Management Framework for Underwater Munitions

2009· article· en· W1972190784 on OpenAlexaboutno aff
Stephen Sayle, Tom Windeyer, Michael Charles, Scott Conrod, M. T. Stephenson

Bibliographic record

VenueMarine Technology Society Journal · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsUnexploded ordnanceUnderwaterAmmunitionRisk assessmentSubmarine pipelineEnvironmental planningEngineeringEnvironmental resource managementMarine engineeringRisk analysis (engineering)Environmental scienceArchaeologyComputer scienceGeographyBusinessComputer security

Abstract

fetched live from OpenAlex

Abstract There are thousands of underwater sites off the North American coasts, and many more worldwide, that contain munitions and chemical warfare agents on the seabed. These sites have resulted from historical sources such as military firing ranges, shipwrecks, and offshore munition disposal. An approach is recommended to manage underwater munition sites, which involves a management framework, site characterization, risk assessment, survey/verification, remedial options analysis, monitoring, and stakeholder consultation. This paper presents a review of historical literature addressing potential health, safety, and environmental issues associated with underwater munition sites. A framework has also been described for risk-based management of underwater munition sites that was developed for a Canadian Department of National Defense underwater Unexploded Ordnance Legacy Site program. The approach is composed of historical and archival research, desktop mapping, identification of data limitations and gap analysis, and development of a preliminary priority risk-based ranking of underwater munition sites in Atlantic Canada.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.257
Teacher spread0.251 · 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 designOther design
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

Citations12
Published2009
Admission routes1
Has abstractyes

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