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Record W2613430224

Special Report: Solomon Islands’ Explosive Legacy

2016· article· en· W2613430224 on OpenAlexaboutno aff
Mette Eliseussen, John Rodsted

Bibliographic record

VenueJMU Scholoraly Commons (James Madison University) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
Fundersnot available
KeywordsMine actionExplosive materialGeographyMining engineeringEngineeringArchaeologyPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

The Solomon Islands encompass over 900 islands scattered across the ocean north of Australia and east of Papua New Guinea. Many of the 500,000 inhabitants still live with unknown quantities of explosive remnants of war (ERW) left behind from combat between Japan and the United States during World War II. Unexploded ordnance (UXO) of both U.S. and Japanese origin remains on some of the nation’s atolls. Since the end of the war, sporadic clearance was undertaken, including through Operation Render Safe, a joint clearance program between Australia, Canada, New Zealand, the United Kingdom, and the United States. There have also been a number of commercial clearance projects. For the last five years, the international clearance organization Golden West Humanitarian Foundation (Golden West), supported by the U.S. Department of State, is working alongside the Royal Solomon Islands Police Force to address the problem on a more systemic scale. This operation focuses on heavily contaminated areas on the island of Guadalcanal.

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)0.004
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.072
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0460.009

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 head0.024
GPT teacher head0.255
Teacher spread0.231 · 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
GenreOther

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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