MétaCan
Menu
Back to cohort
Record W4200593554 · doi:10.9798/kosham.2021.21.6.53

Development of Standard Operating Procedure (SOP) Training Model Using Disaster Safety Communication Network Based on Public Safety Long Term Evolution (PS-LTE)

2021· article· en· W4200593554 on OpenAlexaff
Sangjik Lee, Sung-Geon Park, Chang-Min Ki, Jihong Park, Hyoun-Jung Jo

Bibliographic record

VenueKorean Society of Hazard Mitigation · 2021
Typearticle
Languageen
FieldComputer Science
TopicTechnology and Data Analysis
Canadian institutionsCapcom Vancouver (Canada)
FundersDivision of Civil, Mechanical and Manufacturing InnovationKorea Forest Service
KeywordsCapability Maturity Model IntegrationStandard operating procedureProcess (computing)Training (meteorology)BusinessOperations managementEngineeringProcess managementComputer science

Abstract

fetched live from OpenAlex

This study provides information for developing the domestic standard operating procedure (SOP) to CMMI level 3 or higher by presenting the SOP education and training model development process that systematically utilize the PS-LTE-based disaster safety communication network. The survey was conducted with 113 domestic SOP experts. Results revealed that four strategies can minimize the damage to people's lives and property in a national disaster and develop the domestic SOP level to CMMI level 3 or higher-establishment of governance for the SOPs for disaster safety communication networks; training on SOP once a year; establishment of SOP according to the guidelines; and improvement in the technical field. In the future, if SOP develops to CMMI level 3 or higher, it will contribute to the protection of public safety and property from disasters.

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)0.005
metaresearch head score (Gemma)0.013
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: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.263
Teacher spread0.233 · 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
GenreMethods

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueKorean Society of Hazard MitigationSame topicTechnology and Data AnalysisFrench-language works237,207