{"id":"W4220933785","doi":"10.1029/2021jb022480","title":"Magnitude‐Frequency Distributions and Slip‐History Predictions for Earthquakes Using Cellular Automata and Absorbing Markov Chains","year":2022,"lang":"en","type":"article","venue":"Journal of Geophysical Research Solid Earth","topic":"earthquake and tectonic studies","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Alberta","funders":"","keywords":"Statistical physics; Slip (aerodynamics); Markov chain; Cellular automaton; Scaling; Markov chain Monte Carlo; Discretization; Markov process; Dissipation; Probability distribution; Series (stratigraphy); Mathematics; Monte Carlo method; Computer science; Physics; Statistics; Algorithm; Geology; Mathematical analysis; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000663544,0.0002443404,0.0002634472,0.0006776145,0.0003154306,0.0005935755,0.0005119021,0.0004848556,0.001056295],"category_scores_gemma":[0.004825181,0.0002641069,0.0003496822,0.0003664266,0.0006588425,0.0006678728,0.0002843498,0.0004204545,0.0001106801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009196251,"about_ca_system_score_gemma":0.0005778862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01239979,"about_ca_topic_score_gemma":0.008120756,"domain_scores_codex":[0.9998715,0.00003672092,0.000008649216,0.00002952245,0.00002958409,0.00002401376],"domain_scores_gemma":[0.9973807,0.001934444,0.0002794993,0.0001072814,0.0002213112,0.00007673978],"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":[0.00001389463,0.00001273756,0.002827589,0.000006252906,0.000006661455,0.00002706633,0.00003052794,0.983417,0.0005879098,0.01121451,0.0001085919,0.001747265],"study_design_scores_gemma":[6.582885e-7,0.000001548778,0.0001841756,8.168956e-7,6.58806e-7,0.000002063936,0.000002136662,0.9981412,0.00006064837,0.001589183,0.00001570108,0.000001183491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7021911,0.0001153425,0.2938046,0.0002767624,0.00002363438,0.0000285731,0.0001935765,0.0002168043,0.00314971],"genre_scores_gemma":[0.9965383,0.00002897302,0.003034425,0.000007249931,0.000005497465,0.00001361259,0.00004315181,0.000008098062,0.0003207579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01239979,"threshold_uncertainty_score":0.02465522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06157333613192281,"score_gpt":0.3058741097551804,"score_spread":0.2443007736232576,"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."}}