MétaCan
Menu
Back to cohort
Record W2800909634 · doi:10.9798/kosham.2018.18.3.373

A Study on Improvement of Target Setting Criteria for Disaster Prevention Performance in Jeju Island

2018· article· en· W2800909634 on OpenAlexaff
Joo Suk Ko, Geon Hwa Ryu, Jong Kyung Jang

Bibliographic record

VenueKorean Society of Hazard Mitigation · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPrecipitation Measurement and Analysis
Canadian institutionsFuture Earth
FundersNational Emergency Management Agency
KeywordsEmergency managementFlood mythNatural disasterGeographyAltitude (triangle)Government (linguistics)Flood preventionEnvironmental resource managementEnvironmental scienceEnvironmental planningMeteorologyPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

The government has set the disaster prevention performance target rainfall by the local government (National Emergency Management Agency, 2012) as a flood control policy that can prepare against natural disaster. However, the method of setting the disaster prevention performance target rainfall by municipalities does not reflect the rainfall characteristics of mountainous regions and islands. In this study, rainfall data reflecting the altitude and regional characteristics were analyzed using the meteorological observation system (ASOS, AWS) for Jeju Island. As a result, it was found that the rainfall increases about 1 ~ 4 times as the altitude increases from the lowland to the highland, and the existing disaster prevention performance target standard of Jeju Island does not reflect altitude characteristics. In addition, The target rainfall for disaster prevention performance is presented as dividing the two localities of Jeju city and Seogwipo city into Jeju East, West South and North areas. It is considered that various factors can be reflected in the compatibility assessment stage of rainfall estimation standard.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.276
Teacher spread0.250 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueKorean Society of Hazard MitigationSame topicPrecipitation Measurement and AnalysisFrench-language works237,207