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Record W3005034808 · doi:10.4000/jso.11136

Visualizing Coastal Risks in the Fraser River Delta

2019· article· fr· W3005034808 on OpenAlexfundno aff
Kees Lokman

Bibliographic record

VenueJournal de la Société des océanistes · 2019
Typearticle
Languagefr
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
FundersMitacs
KeywordsGeospatial analysisSaltwater intrusionDeltaCoastal floodCoastal erosionCoastal hazardsTimelineGeographyPopulationRiver deltaSea level riseEnvironmental resource managementIntertidal zoneEnvironmental planningClimate changeCartographyOceanographyGeologyEnvironmental scienceSociologyEngineering

Abstract

fetched live from OpenAlex

Sea level rise (slr) is one of the most existential challenges facing contemporary societies. The potential risks posed by slr involve inundation, population displacement, coastal erosion, wetland loss, saltwater intrusion, and rising water tables. This will have major implication for urban deltas and small island nations. This paper examines how geospatial analysis, mapping and visualization can be used to inform and empower multiple audiences in understanding the fundamental changes future slr will bring to coastal landscapes. Using the Fraser River Delta as a case study, the paper examines a series of visual narratives (maps, models, timelines, and animations) to illustrate the long-term effects of sea level rise on issues such as urban growth, logistics, intertidal habitats, and food security. The article concludes by discussing the success and limitations of the work, as well as its relevance with respect to the challenges of coastal adaptation in the Pacific Islands Region.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.020
GPT teacher head0.306
Teacher spread0.286 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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