{"id":"W4404552283","doi":"10.1038/s41598-024-80136-4","title":"Insights on earthquake nucleation revealed by numerical simulation and unsupervised machine learning of laboratory-scale earthquake","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"earthquake and tectonic studies","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Canada; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Foreshock; Seismology; Earthquake simulation; Geology; Nucleation; Earthquake rupture; Scale (ratio); Shear (geology); Computer science; Fault (geology); Petrology; Aftershock; Physics; Geography; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003180089,0.0001899276,0.0002470234,0.0002638227,0.0002065733,0.0003545017,0.0003734687,0.0003208236,0.0007288301],"category_scores_gemma":[0.001628662,0.0001543057,0.0002881517,0.0001884192,0.0005254738,0.0004465007,0.000249228,0.0004024846,0.00006363982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003363825,"about_ca_system_score_gemma":0.0003123435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003202258,"about_ca_topic_score_gemma":0.002847902,"domain_scores_codex":[0.9999216,0.00002068111,0.000006023095,0.00002105562,0.00001237337,0.00001819725],"domain_scores_gemma":[0.9993914,0.000340802,0.0000885268,0.00009418436,0.00004267922,0.00004240572],"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":[0.00008979689,0.0001740101,0.0269261,0.00006806903,0.00005255534,0.0001421996,0.0001063947,0.9460803,0.01573118,0.00492685,0.0003645012,0.005338048],"study_design_scores_gemma":[0.000002688781,0.00001195804,0.002786189,0.00000125695,0.000002618866,0.00000622736,0.000008150998,0.9952306,0.001111512,0.0007959941,0.00003958896,0.00000319152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874039,0.00004625388,0.01128631,0.00007837465,0.00001030791,0.000007944547,0.0001431052,0.00009403541,0.0009297662],"genre_scores_gemma":[0.9981934,0.00001823495,0.001621149,0.000005935828,0.000003147066,0.000006374278,0.00007177474,0.000006070175,0.00007387966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003202258,"threshold_uncertainty_score":0.006367266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137699702827252,"score_gpt":0.2263079368384677,"score_spread":0.2149309398101952,"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."}}