{"id":"W3097211299","doi":"10.1145/3380867.3426223","title":"SIERA: The Seismic Information Extended Reality Analytics Tool","year":2020,"lang":"en","type":"article","venue":"","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Analytics; Visual analytics; Data science; Human–computer interaction; Visualization; Data mining","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":["insufficient_payload"],"category_scores_codex":[0.0001837644,0.00005397219,0.00007960852,0.00001259624,0.0001103809,0.00006330169,0.000141273,0.00003010029,0.001970822],"category_scores_gemma":[0.00009123337,0.00002691424,0.00005516827,0.0002187677,0.0000240931,0.0001755845,0.000007690663,0.00008713412,0.0009061444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":7.666276e-7,"about_ca_system_score_gemma":0.00001280577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001048722,"about_ca_topic_score_gemma":0.0001705131,"domain_scores_codex":[0.9994593,0.00003748704,0.0001610016,0.00007969185,0.0001458438,0.0001166698],"domain_scores_gemma":[0.999723,0.00004805655,0.00003685851,0.00009905833,0.0000314646,0.00006151774],"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":[0.0000171274,0.000004353727,0.04065517,0.000007464987,0.00002741823,0.000001385555,0.0003528126,0.8009965,6.584441e-7,0.0002640339,0.006723689,0.1509494],"study_design_scores_gemma":[0.0000435899,0.00002731704,0.08970252,5.089296e-7,0.00001551412,6.277152e-7,0.0002039826,0.8988307,0.000002786647,0.0008698803,0.01025462,0.00004794269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5555948,0.0001376053,0.2358259,0.1249364,0.0001522616,0.0002222397,0.00009624908,0.0002906705,0.08274381],"genre_scores_gemma":[0.9904029,0.0000245373,0.0003354313,0.008980275,0.00005500336,1.631292e-7,0.00009365307,3.002284e-7,0.0001077432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4348081,"threshold_uncertainty_score":0.9998718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03506811867571281,"score_gpt":0.2188718258825852,"score_spread":0.1838037072068724,"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."}}