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Record W2203326236 · doi:10.2495/sdp-v10-n4-453-466

Use of geotechnologies in integrated assessment of urban drainage, water resources and urbanization

2015· article· en· W2203326236 on OpenAlexvenueno aff
Edson Patto Pacheco, Alexandra Rodrigues Finotti

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsUrbanizationSanitationDrainageWater resourcesWater resource managementEnvironmental planningIntegrated water resources managementStormwaterEnvironmental scienceEnvironmental resource managementEnvironmental engineeringSurface runoff

Abstract

fetched live from OpenAlex

Water resources historically are impacted by urbanization. Urban drainage, which is an inherent consequence of urbanization, is one of the major elements of impact on water resources in the city. However, urban drainage is almost always treated as a sanitation task, and its intrinsic relation to water resources is often not taken into consideration. As a result, drainage management is performed separately and disconnectedly from urban development management and water resources management. By means of a study on the Campeche aquifer in Florianpolis, we have shown that the disconnected management of these three elements creates serious urban and environmental problems. With an integrative method, we have sought to bring previous studies together, by means of geotechnologies such as GIS, to highlight the key elements for the integration of the sectors responsible for management. The present study suggests the creation of a unified database for the drainage network, hydrography and urban infrastructure, as well as the standardization of projects by means of sanitation, water resources and stormwater management plans. We have also shown how the use of these tools can be extended to other locations, thus proving a very promising integrative methodology.

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)0.001
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 score0.109
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.249
Teacher spread0.223 · 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 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

Citations2
Published2015
Admission routes1
Has abstractyes

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