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Record W4388499267 · doi:10.1038/s41612-023-00513-0

Attributing Venice Acqua Alta events to a changing climate and evaluating the efficacy of MoSE adaptation strategy

2023· article· en· W4388499267 on OpenAlexaff
Davide Faranda, Mireia Ginesta, Tommaso Alberti, Erika Coppola, Marco Anzidei

Bibliographic record

Venuenpj Climate and Atmospheric Science · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsImpact
FundersCentre National de la Recherche ScientifiqueEuropean CommissionDipartimento della Protezione Civile, Presidenza del Consiglio dei Ministri
KeywordsFlooding (psychology)Adaptation (eye)Climate changeSea level riseGeographyMediterranean seaGlobal warmingClimatologyMediterranean climateClimate change adaptationEnvironmental resource managementEnvironmental scienceOceanographyArchaeologyGeologyPsychology

Abstract

fetched live from OpenAlex

Abstract We use analogues of atmospheric patterns to investigate changes in four devastating Acqua Alta (flooding) events in the lagoon of Venice associated with intense Mediterranean cyclones occurred in 1966, 2008, 2018 and 2019. Our results provide evidence that changes in atmospheric circulation, although not necessarily only anthropogenically driven, are linked to the severity of these events. We also evaluate the cost and benefit of the MoSE system, which was designed to protect against flooding. Our analysis shows that the MoSE has already provided protection against analogues of the most extreme event, which occurred in 1966. These findings have significant implications for the future of Venice and other coastal cities facing similar challenges from rising sea levels due to extreme events. This study also provides a pathway to evaluate the effectiveness of adaptation in a scenario more frequent and intense extreme events if higher global warming levels will be reached.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.341
Teacher spread0.271 · 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 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

Citations24
Published2023
Admission routes1
Has abstractyes

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Same venuenpj Climate and Atmospheric ScienceSame topicTropical and Extratropical Cyclones ResearchFrench-language works237,207