{"id":"W4402035628","doi":"10.32920/26882482.v1","title":"Generative Design of Geospatial Interventions With Automated Machine Learning and Bayesian Optimization","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Geospatial analysis; Bayesian optimization; Generative grammar; Computer science; Psychological intervention; Machine learning; Artificial intelligence; Bayesian probability; Generative Design; Data science; Engineering; Geography; Psychology; Operations management; Remote sensing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00341306,0.0009130338,0.0009395768,0.001022019,0.000592114,0.002025247,0.002192144,0.001407708,0.005972704],"category_scores_gemma":[0.009778943,0.001039794,0.001652166,0.0006944183,0.002382888,0.001963607,0.002550593,0.001735488,0.0006218967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002132243,"about_ca_system_score_gemma":0.00246969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005543796,"about_ca_topic_score_gemma":0.006535048,"domain_scores_codex":[0.9974784,0.001254159,0.0001225539,0.0004749032,0.0004825963,0.0001872713],"domain_scores_gemma":[0.9950088,0.003516985,0.0003736681,0.0004968733,0.0004791274,0.0001246185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004714978,0.00008032129,0.000941998,0.0001677239,0.00006093598,0.00008540141,0.0002713309,0.8780409,0.001398541,0.08762382,0.0006725995,0.03060917],"study_design_scores_gemma":[0.00002464768,0.00004468782,0.0001798542,0.00003211923,0.00001849725,0.00001964893,0.00005951796,0.9555859,0.0008106243,0.04059043,0.002619796,0.00001421229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007969853,0.00009184987,0.9871343,0.0002686822,0.00001944873,0.0001493647,0.0000468408,0.000357527,0.003962152],"genre_scores_gemma":[0.451328,0.0002866244,0.5425833,0.0002244786,0.00002282802,0.0009631992,0.0001573989,0.0002062937,0.004227986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005972704,"threshold_uncertainty_score":0.01998073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03124892190214539,"score_gpt":0.3200601993625975,"score_spread":0.2888112774604521,"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."}}