{"id":"W2071120002","doi":"10.3390/ijgi3031077","title":"Spatial Representation of Coastal Risk: A Fuzzy Approach to Deal with Uncertainty","year":2014,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geospatial analysis; Fuzzy set; Representation (politics); Computer science; Spatial analysis; Fuzzy logic; Set (abstract data type); Risk assessment; Data mining; Uncertainty analysis; Geographic information system; Scale (ratio); Geography; Mathematics; Artificial intelligence; Statistics; Cartography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001743382,0.0001177267,0.0002496591,0.0006394002,0.0002432563,0.0001622996,0.0004399153,0.00007340984,0.00001956969],"category_scores_gemma":[0.0005238242,0.00009649865,0.0001235038,0.0003820872,0.0001397416,0.002770524,0.00006059892,0.0001681818,0.00002963291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001207957,"about_ca_system_score_gemma":0.0001719862,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006784908,"about_ca_topic_score_gemma":0.001066342,"domain_scores_codex":[0.9968698,0.0001599532,0.001003004,0.00006537879,0.001714107,0.0001877827],"domain_scores_gemma":[0.9946558,0.0001533158,0.001766982,0.0001029644,0.003205142,0.0001157227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002234863,0.0001900804,0.1555448,0.00008382801,0.001076767,0.00000212634,0.2380487,0.07229724,0.0000440932,0.1107487,0.005537335,0.4141915],"study_design_scores_gemma":[0.008363033,0.001440255,0.3576321,0.0005095211,0.000226444,0.0001671821,0.1609034,0.009219367,0.0004808567,0.004138481,0.456002,0.0009173328],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4049628,0.000009527902,0.4444565,0.002056385,0.001379204,0.0006452895,0.0001074455,0.00004674317,0.1463361],"genre_scores_gemma":[0.9924575,0.00002832974,0.006822736,0.0001830484,0.0004162586,0.00001045781,0.00003931037,0.000003891514,0.00003845144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5874947,"threshold_uncertainty_score":0.999829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01125434366504189,"score_gpt":0.2799574109122084,"score_spread":0.2687030672471665,"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."}}