{"id":"W3023669052","doi":"10.15530/urtec-2016-2444366","title":"Using Depletion-Zone Microseismicity to Understand Producing Volumes","year":2016,"lang":"en","type":"article","venue":"","topic":"Earthquake Detection and Analysis","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Microsemi (Canada)","funders":"","keywords":"Geology; Computer science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001886937,0.00008087372,0.000103729,0.000107246,0.0001801971,0.0000547251,0.00007552168,0.00002968957,0.005363163],"category_scores_gemma":[0.00004446148,0.00004985004,0.000050509,0.0002863152,0.0000335567,0.0001509253,0.000006266576,0.00003240728,0.0006879292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007599047,"about_ca_system_score_gemma":0.00002336813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003969099,"about_ca_topic_score_gemma":0.01193027,"domain_scores_codex":[0.9992276,0.00003612202,0.0001351142,0.0002545501,0.0001409075,0.0002056338],"domain_scores_gemma":[0.9996156,0.00004504825,0.00003051654,0.0001462249,0.00003687833,0.0001257726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004194042,0.00001664199,0.7475213,0.000009387059,0.00005247422,0.000006742842,0.0003894052,0.003565653,0.02647428,0.00003778602,0.001867464,0.2200169],"study_design_scores_gemma":[0.0009242928,0.0002513659,0.8917554,0.000107343,0.00008002094,0.0000648686,0.001896085,0.03494411,0.03424649,0.0006825653,0.0340856,0.0009618752],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8612725,0.00003520231,0.136046,0.001124212,0.0001122437,0.0000650622,0.00001554169,0.00005717114,0.001272002],"genre_scores_gemma":[0.9907728,0.000008252408,0.004022454,0.000636339,0.00008449912,8.5935e-8,0.000002727365,0.000002263268,0.004470525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.219055,"threshold_uncertainty_score":0.995546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03622139738684505,"score_gpt":0.236586654675811,"score_spread":0.2003652572889659,"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."}}