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Record W2774908743 · doi:10.5150/cmcm.2017.033

Towards an understanding and operational early warning of the Adriatic meteotsunamis: Project MESSI

2017· article· en· W2774908743 on OpenAlexaff
Jadranka Šepić, Ivica Vilibić, Gordana Beg Paklar, Vlado Dadić, Cléa Denamiel, Natalija Dunić, Tomislav Džoić, M. Gačić, Kristian Horvath, Damir Ivanković, Dalibor Jelavic, Hrvoje Kalinić, Žarko Kovač, Vedrana Kovačević, Toni Masce, Frano Matić, Iva MEDJUGORAC, Hrvoje Mihanović, S. Monserrat, Stipe Muslim, Alexander B. Rabinovich, Martina Tudor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsWarning systemComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

The paper presents the architecture and major achievements of the project MESSI, aiming to build a reliable prototype of a meteotsunami warning system based on realtime measurements, operational atmosphere and ocean modelling and real-time decision-making process, using knowledge acquired from analysis of historical destructive events.A number of tide gauge and microbarograph stations were installed in the middle Adriatic area, whilst synoptic patterns and numerical weather prediction mesoscale models have been examined to establish connection between atmospheric forcing and meteotsunami waves.Project outcomes will be highly beneficial for endangered coastal communities, in a sense of rising timely alarms, for planning of construction works along the coastline (roads, marinas, piers, etc.), for the navigation safety, educating people and raising awareness in endangered areas.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.078
GPT teacher head0.278
Teacher spread0.200 · 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 designSimulation or modeling
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

Citations4
Published2017
Admission routes1
Has abstractyes

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