MétaCan
Menu
Back to cohort
Record W2526494366 · doi:10.2495/safe-v6-n3-616-626

Design with floods: from defence against a ‘Natural’ threat to adaptation to a human-natural process

2016· article· en· W2526494366 on OpenAlexvenueno aff
Liliane Hobeica, Pedro Pinto Santos

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsNatural (archaeology)Adaptation (eye)Process (computing)Natural disasterNatural hazardRisk analysis (engineering)Computer sciencePsychologyBusinessGeography

Abstract

fetched live from OpenAlex

Recognizing that traditional flood management interventions focus on defence, attempting to eliminate contingencies in the urban relationship with rivers, an emergent perspective, spearheaded by spatial design, seeks to deal with floods through a more holistic framework. In contrast to the prevalent 'design against floods' approach that targets either the hazard or the exposure components of flood risk, 'design with floods' focuses as well on the assets at stake (including the built envelopes of exposed people and activities, usually covered under the term vulnerability), duly acknowledging the intertwining of natural and human processes. Using a multiple case study comprising three European flood-prone urban projects, we explore potentials of spatial design as an adaptation tool that goes beyond flood protection to foster wider societal gains. Our analyses have so far suggested that 'design with floods' requires a positive stance through which problem-solving and sense-making approaches are merged to provide both safety and urbanity (enriched urban realm and experience), without eliminating floods per se, accepted as a complex hybrid process.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.295

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 science0.0000.000
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 head0.007
GPT teacher head0.229
Teacher spread0.221 · 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 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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Safety and Security EngineeringSame topicFlood Risk Assessment and ManagementFrench-language works237,207