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Record W2592487031

Applying the “WSUD potential”-tool in the framework of the Copenhagen Climate Adaptation and Cloudburst Management Plans

2016· preprint· en· W2592487031 on OpenAlexaff
Sara Maria Lerer, Herle Mo Madsen, Jonas Smit Andersen, Hannibal Rasmussen, Hjalte Jomo Danielsen Sørup, Karsten Arnbjerg‐Nielsen, Peter Steen Mikkelsen

Bibliographic record

VenueTechnical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · 2016
Typepreprint
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsNicolet Chartrand Knoll (Canada)
Fundersnot available
KeywordsAdaptation (eye)Computer scienceClimate change adaptationEnvironmental resource managementEnvironmental scienceClimate changeGeologyOceanography
DOInot available

Abstract

fetched live from OpenAlex

Les aménagements urbains alternatifs pour la gestion de l'eau (Water Sensitive Urban Design, WSUD) sont toujours dans la phase « opportunité » du processus de stabilisation en cours à Copenhague, Danemark.Ceci indique que des controverses existent encore autour de leur mise en œuvre et que le cadre législatif et réglementaire existant n'est pas complètement adapté à ces nouvelles technologies.En 2015, au Danemark, les propriétaires fonciers privés ont pu obtenir un financement pour mettre en place des mesures d'adaptation climatique, de la part des sociétés de gestion des eaux, et ce jusqu'à 100% des coûts de construction, entrainant une course aux candidatures pour ce type d'aide.Cette étude propose un compte-rendu rapide du cadre réglementaire pour l'adaptation climatique à Copenhague.Nous discutons ensuite l'interaction et l'adéquation de différents scénarios de gestion alternative avec ce cadre, dans une étude de cas.L'impact des différents scénarii est évalué par le biais de l'outil « WSUD potential », qui utilise l'approche « Trois Points ».Les résultats indiquent un schisme entre le Plan de Gestion des Pluies Intenses de la ville d'une part et son Plan d'Adaptation Climatique et objectifs de niveau de service, d'autre part.Ce schisme peut notamment entrainer un surdimensionnement du système de gestion des eaux pluviales.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.019
GPT teacher head0.217
Teacher spread0.198 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

Explore more

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