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Record W2593430563 · doi:10.1080/19475705.2017.1294114

Development of a GIS coastal land-use planning tool for coastal erosion adaptation based on the exposure of buildings and infrastructure to coastal erosion, Québec, Canada

2017· article· en· W2593430563 on OpenAlexaffabout
Christian Fraser, Pascal Bernatchez, Steeve Dugas

Bibliographic record

VenueGeomatics Natural Hazards and Risk · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsCoastal erosionShoreGeographic information systemCoastal hazardsErosionStormLand useStorm surgeEnvironmental scienceHydrology (agriculture)Environmental resource managementGeographySea level riseRemote sensingGeologyClimate changeOceanographyCivil engineeringMeteorologyGeomorphologyEngineering

Abstract

fetched live from OpenAlex

This study presents the development of a geographic information system (GIS) land-use planning tool for coastal areas based on the calculated exposure to coastal erosion of buildings and infrastructure. Responding to the needs of land-use planners, who are involved in the project from the beginning, this tool facilitates identification of adaptation solutions based on coastal sensitivity to erosion. All buildings, roads, railways, aqueducts, sewer systems, hiking trails and bicycle routes were mapped at high resolution, and an exposure value was assigned to each for seven time horizons between 2015 and 2100. The calculations were based on three parameters: (1) the distance between the structure and the shoreline or coastline; (2) the most likely shoreline or coastline migration rate for each coastal geomorphology behaviour unit (CGBU); and (3) the maximum event retreat measured during a storm for each type of coast. This method was applied to Baie des Chaleurs in Québec, Canada. The area comprises 11 municipalities with a total of 105 km of coast. The approach not only produces current and future portraits of building and infrastructure exposed to erosion, but also provides an original land-use planning and intervention tool for coastal areas.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.477

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 studies0.0010.000
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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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

Citations33
Published2017
Admission routes2
Has abstractyes

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