{"id":"W4413303424","doi":"10.21203/rs.3.rs-7313707/v1","title":"Global machine-learning detection of submarine calderas","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Bộ Giáo dục và Ðào tạo; Ministry of Education, India; Ministry of Education - Singapore","keywords":"Caldera; Submarine; Geology; Artificial intelligence; Computer science; Seismology; Oceanography; Volcano","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007820663,0.0002329247,0.0004527841,0.0003993493,0.0001444937,0.00007018791,0.0004145894,0.0005711296,0.0001979538],"category_scores_gemma":[0.0006847667,0.0002140182,0.0003291665,0.0009952933,0.0001446894,0.00004517784,0.0006794554,0.002691772,0.00001333312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005768053,"about_ca_system_score_gemma":0.0001051063,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008011614,"about_ca_topic_score_gemma":0.003569233,"domain_scores_codex":[0.99778,0.0003461674,0.0003918506,0.0003733135,0.0006961394,0.0004124924],"domain_scores_gemma":[0.998548,0.0002542466,0.00004193623,0.0004986752,0.0005530835,0.0001041061],"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":[0.0001156754,0.00007498843,0.03851822,0.01218938,0.0009827014,0.00002388509,0.000435516,0.8681642,0.001733687,0.0009633208,0.0004313293,0.07636704],"study_design_scores_gemma":[0.0004390696,0.0001845684,0.04901378,0.00168824,0.0001692827,0.000008441772,0.0005408849,0.9068627,0.01807963,0.01443356,0.007885702,0.0006941719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9627984,0.006635453,0.006954458,0.0003088715,0.001144965,0.0009441806,0.0005989724,0.000694936,0.01991979],"genre_scores_gemma":[0.9977782,0.001166195,0.0001720456,0.000002276352,0.0001142526,0.00004023328,0.0001109158,0.0000138906,0.0006019379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07567286,"threshold_uncertainty_score":0.9996091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02747210932646487,"score_gpt":0.3428227707385667,"score_spread":0.3153506614121018,"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."}}