{"id":"W2126311075","doi":"10.1190/1.3627522","title":"Akaike information criterion applied to detecting first arrival times on microseismic data","year":2011,"lang":"en","type":"article","venue":"","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Microseismic Industry Consortium; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Microseism; Akaike information criterion; Geology; Seismology; Geophone; Induced seismicity; Noise (video); Computer science; Artificial intelligence; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002259952,0.00010688,0.0001045653,0.0001069793,0.0002712763,0.00007080004,0.0008673209,0.00004692673,0.0000495554],"category_scores_gemma":[0.00005404762,0.00009123651,0.00001645852,0.0001654059,0.00002669924,0.0007373587,0.0007026493,0.00008719815,0.0012764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001060196,"about_ca_system_score_gemma":0.00001383319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000108408,"about_ca_topic_score_gemma":0.00002830927,"domain_scores_codex":[0.9992453,0.00001385483,0.0001746979,0.0002462612,0.0001001439,0.0002196881],"domain_scores_gemma":[0.9990996,0.00005538535,0.00004598379,0.0007196447,0.00002883688,0.00005061002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002333004,0.0001489125,0.001962317,0.0000649837,0.0001222392,0.00001498238,0.04367871,0.0002457177,0.000809663,0.06311362,0.0512121,0.8383934],"study_design_scores_gemma":[0.002899225,0.001861241,0.2883291,0.0001742211,0.0000590442,0.0001667209,0.003233485,0.08110499,0.09004219,0.01520802,0.5141612,0.002760544],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.134155,0.00002186322,0.7863695,0.001988132,0.0009045632,0.0003397012,0.000008524002,0.0004840307,0.07572866],"genre_scores_gemma":[0.9529235,0.000003312855,0.04233053,0.004569888,0.00003608737,0.000008711933,0.00000739401,0.000003219046,0.0001173229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8356329,"threshold_uncertainty_score":0.9995012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05396405769169121,"score_gpt":0.2384453160656296,"score_spread":0.1844812583739384,"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."}}