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Record W2346899148 · doi:10.3389/fmars.2016.00057

Modern Approaches in Meteotsunami Research and Early Warning

2016· article· en· W2346899148 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Marine Science · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsFisheries and Oceans Canada
FundersRussian Science FoundationShirshov Institute of Oceanology, Russian Academy of SciencesDivision of Ocean SciencesHrvatska Zaklada za ZnanostUnity through Knowledge Fund
KeywordsWarning systemNatural hazardRelevance (law)MeteorologyEnvironmental scienceHazardClimatologyEarly warning systemEnvironmental resource managementGeographyEngineeringGeologyTelecommunicationsEcologyPolitical science

Abstract

fetched live from OpenAlex

The understanding of meteotsunamis - significant atmospherically generated long ocean waves in the tsunami frequency band - has advanced considerably during the last two decades. Scientists and specialists use near-field in situ data and remote observations, as well as atmospheric and ocean modelling, to study destructive events. The phenomenon has been reported and investigated worldwide, indicating its relevance within other marine natural hazards and demonstrating the urgent need for meteotsunami warning systems for certain countries. In this paper we summarize the present knowledge of the phenomenon, identify particular research gaps, and propose near-future critical components of meteotsunami research. We emphasize a potential concept of merging yet-to-be-developed meteotsunami warning systems and existing tsunami or multi-hazard early warning systems.

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.

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)0.002
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 score0.345
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.067
GPT teacher head0.261
Teacher spread0.194 · 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