{"id":"W3115695683","doi":"10.3138/cart-2020-0003","title":"3D Geovisualization Interfaces as Flood Risk Management Platforms: Capability, Potential, and Implications for Practice","year":2020,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Geovisualization; Workflow; Visualization; Computer science; Data science; Geospatial analysis; Flood myth; Interface (matter); Risk management; Information visualization; Geography; Remote sensing; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001011048,0.0002127051,0.0001506345,0.0002500826,0.0009263589,0.0007017693,0.0003890571,0.0000841927,0.00005851038],"category_scores_gemma":[0.0003057423,0.0001667567,0.0001266606,0.0004063401,0.0001921327,0.002166227,0.0002659928,0.0001532208,0.000008698935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004263135,"about_ca_system_score_gemma":0.00001559545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001016644,"about_ca_topic_score_gemma":0.00004109101,"domain_scores_codex":[0.9983318,0.00005279844,0.000637886,0.0002582757,0.0004729868,0.0002461835],"domain_scores_gemma":[0.9986073,0.0001467555,0.0006155997,0.0001461024,0.0003258384,0.000158441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002271896,0.0005660756,0.1230726,0.0004521766,0.002819434,0.000003762523,0.01358258,0.02887051,0.0003665244,0.4008402,0.03580885,0.3913454],"study_design_scores_gemma":[0.005435498,0.001121407,0.04765864,0.00007385023,0.001141323,0.0001620456,0.009326817,0.1352613,0.00012859,0.06716532,0.7317022,0.0008230038],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2383759,0.0003903626,0.7312427,0.02354235,0.001061186,0.003234459,0.0001866498,0.0001492776,0.001817097],"genre_scores_gemma":[0.976516,0.009429571,0.006560403,0.006453877,0.0001913238,0.0003106371,0.0004719979,0.00002335061,0.00004282288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7381401,"threshold_uncertainty_score":0.7124897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009087597729123433,"score_gpt":0.2882672760945434,"score_spread":0.27917967836542,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}