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Record W1981806198 · doi:10.4031/mtsj.43.4.14

A Global Prospective On Underwater Munitions

2009· article· en· W1981806198 on OpenAlexaboutno aff
Terrance P. Long

Bibliographic record

VenueMarine Technology Society Journal · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaUnderwaterFish <Actinopterygii>International watersSteering committeePolitical scienceAeronauticsEngineeringFisheryEnvironmental planningLawEnvironmental scienceGeographyArchaeologyEngineering management

Abstract

fetched live from OpenAlex

This article introduces an issue of the Marine Technology Society Journal focused on the understanding and ongoing exchange of information on underwater munitions. In June 2003, the Canadian Standing Senate Committee for Fisheries and Oceans on Fish Habitat listened to a panel of witnesses urgently request greater federal involvement from entities other than the Department of National Defense in addressing the issue of underwater munitions in both Canadian and international waters. When there was a lack of response from governments, as well as the United Nations, the First International Conference on Chemical and Conventional Munitions convened in Halifax, Nova Scotia, Canada in October 2007. Over 190 delegates from 14 countries affected by underwater munitions attended the conference and collectively submitted more than 50 papers. The author discusses the history of underwater munitions and explores relationships among international stakeholders which allow them to build on each others' experiences. The increasing awareness of and interest in identification and assessment of potential risks to human health and the environment by underwater munitions is explored.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.004
Scholarly communication0.0050.008
Open science0.0010.006
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0370.003

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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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

Citations14
Published2009
Admission routes1
Has abstractyes

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