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Record W2613516707

Groundwater, geothermal modelling and monitoring at city-scale : reviewing European practice and knowledge exchange : TU1206 COST sub-urban WG2 report

2017· article· en· W2613516707 on OpenAlexaboutno aff
H.C. Bonsor, Peter Dahlqvist, L. Moosman, Nick Classen, Jannis Epting, Peter Huggenberger, A. Garica-Gil, Mitja Janža, Gert Laursen, Roelof Stuurman, Constantin Radu Gogu

Bibliographic record

VenueNERC Open Research Archive (Natural Environment Research Council) · 2017
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental planningResource (disambiguation)Urban planningGroundwaterScale (ratio)Geothermal gradientEnvironmental resource managementGeographyBusinessEnvironmental scienceCivil engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

The need for cities to make more effective use of the subsurface on which they stand, is increasingly being recognised in Europe and further afield to be essential for future cities to be sustainable and more resilient [1,2]. However, city planning worldwide remains largely 2D, with very few cities having any substantial subsurface planning or Masterplans – the cities of Helsinki, Montreal, Singapore being rare exceptions [3,4]. The consequences of inadequate consideration and planning of the subsurface are far-reaching, in economic, environmental and social terms. Across Europe, poor understanding of ground conditions is recognised as the largest single cause of construction project delay and overspends [5]. Management of urban groundwater and shallow geothermal energy resources is becoming increasingly important as cities are increasingly looking to use these resources to meet current and future energy and heating and water needs. Whilst these are, alongside potential underground building space, the two most important resources for future cities, the monitoring and regulation of these resource is widely variable across Europe.
\nFor subsurface opportunities such as groundwater and geothermal energy to be realised and utilised to greatest effect to support growing city populations and infrastructure, city planners must be both aware of, and have some understanding of the resources, available data and research, and both the opportunities and risks which the resources provide to city development [6,7]. To supply this understanding to city municipalities and others, geological surveys must have robust datasets of groundwater and geothermal resources at city-scale, and the relevant knowledge and understanding from these data must be made accessible to inform subsurface planning in appropriate datasets relevant to different scale of interest in different planning stages. What density and frequency of data are required for a robust understanding of a city’s groundwater and geothermal resources will be different in different cities, according to the complexity of the resources, and the intensity of subsurface use and demands on the resources. Indeed, no one design of city-scale monitoring or modelling of ground-water and -heat resources is appropriate for all cities, or for all monitoring objectives. However, the guiding principles of good practice for developing robust city-scale monitoring, and datasets are widely applicable, as are the key principles for ensuring these data inform city planning processes.
\nThis report provides an initial review of existing examples of current practices in Europe with respect to groundwater and geothermal monitoring and modelling, as a resource for other cities to learn from and build upon. The report also provides an overview of some of the different practices used for communicating groundwater and geothermal energy data and knowledge to inform urban planning and management

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)0.030
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0300.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0020.007
Research integrity0.0000.003
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 head0.133
GPT teacher head0.346
Teacher spread0.213 · 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.

Study designNot applicable
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

Citations3
Published2017
Admission routes1
Has abstractyes

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