{"id":"W2103735750","doi":"10.3997/2214-4609.20141054","title":"Refinement of Arrival-time Picks Using an Iterative, Cross-correlation Based Workflow","year":2014,"lang":"en","type":"article","venue":"Proceedings","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":1,"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; Workflow; Arrival time; Computer science; Cross-correlation; Algorithm; Noise (video); Monte Carlo method; Real-time computing; Data mining; Geology; Seismology; Statistics; Artificial intelligence; Mathematics; Database; Engineering","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":[0.003895407,0.002027989,0.001482403,0.002868547,0.001262036,0.002148202,0.002500828,0.001153251,0.005089078],"category_scores_gemma":[0.01316982,0.001259958,0.001695808,0.001864575,0.0008123689,0.001344745,0.002824127,0.001874643,0.005185158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009716867,"about_ca_system_score_gemma":0.004151502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01037559,"about_ca_topic_score_gemma":0.01082863,"domain_scores_codex":[0.9973885,0.0003297073,0.0002800854,0.0006155629,0.001104224,0.0002818619],"domain_scores_gemma":[0.9885706,0.003217058,0.001019547,0.002498441,0.004186663,0.000507766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009274231,0.0003810877,0.0104881,0.000352133,0.0002919979,0.0007664657,0.001206679,0.2168354,0.1756935,0.006067112,0.006307596,0.5806825],"study_design_scores_gemma":[0.00006924038,0.0001290842,0.002785606,0.00002284656,0.0000552477,0.0003514212,0.0001381316,0.920441,0.06674775,0.003685727,0.005467552,0.0001064166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01425175,0.00003978395,0.9781502,0.0000447262,0.00002134874,0.00008304808,0.0001250286,0.006755256,0.0005289723],"genre_scores_gemma":[0.07985032,0.00003862358,0.9171287,0.00004164385,0.00002218772,0.0001151898,0.0007635362,0.001138112,0.0009015809],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01037559,"threshold_uncertainty_score":0.02063042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084387591991132,"score_gpt":0.26767567809722,"score_spread":0.2468318021773087,"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."}}