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Record W2257048481 · doi:10.1017/s1049023x00025528

Theme 3. Sharing Pacific-Rim Experiences in Disasters: Summary and Action Plan

2001· article· en· W2257048481 on OpenAlexaff
Catherine J. Hickson, Michael J. Schull, Emilio Huertas Arias, Yasufumi Asai, Jih-Chang Chen, Henry Kam Hong Cheng, Noboru Ishii, Tatsuya Kinugasa, Patrick Chow‐In Ko, Yuichi Koido, Yoshio Murayama, Poon Wai Kwong, Takashi Ukai

Bibliographic record

VenuePrehospital and Disaster Medicine · 2001
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreGeological Survey of Canada
Fundersnot available
KeywordsTheme (computing)Plan (archaeology)Action planAction (physics)Identification (biology)Public relationsSet (abstract data type)Political scienceOperations researchBusinessEngineeringComputer scienceGeographyManagement

Abstract

fetched live from OpenAlex

INTRODUCTION: The discussions in this theme provided an opportunity to address the unique hazards facing the Pacific Rim. METHODS: Details of the methods used are provided in the preceding paper. The chairs moderated all presentations and produced a summary that was presented to an assembly of all of the delegates. Since the findings from the Theme 3 and Theme 7 groups were similar, the chairs of both groups presided over one workshop that resulted in the generation of a set of action plans that then were reported to the collective group of all delegates. RESULTS: The main points developed during the presentations and discussion included: (1) communication, (2) coordination, (3) advance planning and risk assessment, and (4) resources and knowledge. DISCUSSION: Action plans were summarized in the following ideas: (1) plan disaster responses including the different types, identification of hazards, focusing training based on experiences, and provision of public education; (2) improve coordination and control; (3) maintain communications, assuming infrastructure breakdown; (4) maximize mitigation through standardized evaluations, the creation of a legal framework, and recognition of advocacy and public participation; and (5) provide resources and knowledge through access to existing therapies, the media, and increasing and decentralizing hospital inventories. CONCLUSIONS: The problems in the Asia-Pacific rim are little different from those encountered elsewhere in the world. They should be addressed in common with the rest of the world.

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)0.017
metaresearch head score (Gemma)0.012
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0050.004
Open science0.0030.009
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0140.004

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 head0.095
GPT teacher head0.384
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
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".

Quick stats

Citations3
Published2001
Admission routes1
Has abstractyes

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