{"id":"W4401150042","doi":"10.1111/jfr3.13026","title":"Reimagining nature‐based coastal adaptation: A nested framework","year":2024,"lang":"en","type":"article","venue":"Journal of Flood Risk Management","topic":"Coastal wetland ecosystem dynamics","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada; Fisheries and Oceans Canada; Department of Agriculture, Nova Scotia","keywords":"Adaptation (eye); Climate change adaptation; Nested set model; Computer science; Environmental resource management; Geography; Environmental science; Oceanography; Geology; Climate change; Psychology; Data mining","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0006863475,0.0001548163,0.0001851902,0.0001502119,0.00009591944,0.0001708772,0.0002826421,0.00008316444,0.0004936227],"category_scores_gemma":[0.00004079878,0.0001114221,0.0001596001,0.000464407,0.00003551156,0.0003171221,0.0001821638,0.0007598971,0.0002202905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001583436,"about_ca_system_score_gemma":0.00001965346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005747454,"about_ca_topic_score_gemma":0.0002226557,"domain_scores_codex":[0.9984117,0.0000753779,0.0004487624,0.0002123844,0.0006244308,0.0002273022],"domain_scores_gemma":[0.9993223,0.0001072992,0.0002313888,0.0002084543,0.00001727112,0.0001133077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003707593,0.0004987102,0.06263653,0.0006391569,0.0009861259,0.004770143,0.002110343,0.4069919,0.000105632,0.006066914,0.02716276,0.487661],"study_design_scores_gemma":[0.001139103,0.0004445433,0.04614804,0.001290381,0.000615035,0.0002418211,0.001535211,0.7688468,0.00005252375,0.01105189,0.1681063,0.0005283623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.29504,0.001500728,0.6580319,0.003313135,0.006243612,0.00065614,0.00004632729,0.0002013447,0.03496677],"genre_scores_gemma":[0.9575139,0.0001658666,0.04143203,0.0001476324,0.0002029192,0.000004409126,0.000003586088,0.00002401916,0.0005056038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6624739,"threshold_uncertainty_score":0.5404822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005190373857995415,"score_gpt":0.2299258906833144,"score_spread":0.224735516825319,"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."}}