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Record W4410227669 · doi:10.11159/ijci.2025.005

Enhancing Design Standard of Agricultural Dam to Reduce Flood Risk

2025· article· en· W4410227669 on OpenAlexvenueno aff
Baeg Lee, Byoung-Han Choi

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsFlood mythAgricultureWater resource managementBusinessEnvironmental planningEnvironmental scienceRisk analysis (engineering)GeographyArchaeology

Abstract

fetched live from OpenAlex

The Agricultural Production Infrastructure Design Standard has not been revised for more than 10 years, except the Drainage Part ( 2012), so it needs to be upgraded to respond to changes in the agricultural environment, such as climate change and agricultural system conversion.In many countries such as the United States, Europe, and Japan, standardized codes have been established for the maintenance of agricultural infrastructure.These codes reflect the latest research and technological trends, and they are updated regularly to ensure efficient operation and management.The need for the revision of agricultural infrastructure design standards is necessary to reflect the latest technologies for responding to agricultural disasters (disasters caused by climate change such as droughts and floods), and to strengthen the standards for reducing flood risk.This study aims to revise the overall contents of the design standards for agricultural dams such as climate change response to reduce flood risk, economic feasibility analysis, and new technology introduction, incorporating the deliberations of the Central Construction Deliberation Committee, the construction standards for emergency discharge facilities, smart management, the Huff method, and the AHP method for the economic analysis.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.010
GPT teacher head0.259
Teacher spread0.249 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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