{"id":"W4392463875","doi":"10.1080/17538947.2024.2311325","title":"Enhancing flood-prone area mapping: fine-tuning the K-nearest neighbors (KNN) algorithm for spatial modelling","year":2024,"lang":"en","type":"article","venue":"International Journal of Digital Earth","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Korea Institute for Advancement of Technology; Ministry of Trade, Industry and Energy","keywords":"Flood myth; Geography; k-nearest neighbors algorithm; Algorithm; Cartography; Computer science; Data mining; Artificial intelligence; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002978065,0.000129635,0.0001208648,0.0000987573,0.00008130915,0.0006106388,0.0004332359,0.00002896885,0.0002111129],"category_scores_gemma":[0.00003199436,0.00008963955,0.0001671313,0.0001062189,0.00005807701,0.0009130402,0.0001771724,0.0001805391,0.00005926637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008859142,"about_ca_system_score_gemma":0.00003451019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006763599,"about_ca_topic_score_gemma":0.00007149278,"domain_scores_codex":[0.9985942,0.00001197526,0.0003808727,0.0001687289,0.0006536533,0.0001906066],"domain_scores_gemma":[0.9995184,0.0001286924,0.0001469351,0.00008630041,0.00005331321,0.00006637173],"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":[0.00005713608,0.0001708578,0.001985586,0.00002061793,0.0005786371,0.0002098963,0.001047218,0.1702053,0.001411583,0.001248116,0.002756473,0.8203086],"study_design_scores_gemma":[0.000478641,0.0001636994,0.0008510454,0.0001886619,0.00003684515,0.00006496539,0.0003051765,0.926727,0.0009174787,0.002847473,0.06723483,0.0001841973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05399094,0.0001264386,0.9388961,0.001279778,0.001452594,0.0001615047,0.00002943215,0.00002398881,0.00403927],"genre_scores_gemma":[0.9844534,0.00004339782,0.01340422,0.0000820968,0.0006400985,0.000006643436,0.00001594658,0.00001884336,0.001335375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9304624,"threshold_uncertainty_score":0.5888404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01900810242261628,"score_gpt":0.2479148184942059,"score_spread":0.2289067160715896,"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."}}