{"id":"W1970212439","doi":"10.1071/aseg2015ab018","title":"Randomized algorithms in exploration seismology Petroleum keynote paper","year":2015,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital","funders":"","keywords":"Algorithm; Computer science; Inversion (geology); Compressed sensing; Process (computing); Field (mathematics); Data science; Data mining; Machine learning; Artificial intelligence; Industrial engineering; Geology; Mathematics; Seismology; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.001408997,0.0001574797,0.0003299156,0.0001844597,0.00005127555,0.000049971,0.0001685662,0.0001108203,0.0004034882],"category_scores_gemma":[0.0002887193,0.0001279223,0.00007230826,0.0001408902,0.0001292354,0.000744917,0.00001034632,0.0002350126,0.0005350733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001284148,"about_ca_system_score_gemma":0.00009280548,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008816889,"about_ca_topic_score_gemma":0.0001336756,"domain_scores_codex":[0.9985008,0.0002307989,0.00038782,0.0002841975,0.0002700296,0.0003263438],"domain_scores_gemma":[0.9992383,0.0002085368,0.0001288265,0.0002003579,0.00005399398,0.0001700156],"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":[0.01053336,0.0001324101,0.0009891335,0.00001598715,0.00003793449,0.0003660329,0.001140117,0.01133418,0.0000227175,0.0003177605,0.03296025,0.9421501],"study_design_scores_gemma":[0.1975455,0.0006036356,0.1391371,0.0001355807,0.00008801866,0.0002858053,0.002789657,0.2784395,0.006337466,0.1804328,0.1926522,0.001552727],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8521793,0.002395076,0.007554357,0.02093369,0.003307096,0.001372131,0.00007277216,0.001061542,0.1111241],"genre_scores_gemma":[0.9923589,0.00008953069,0.003208046,0.003465261,0.0001050309,0.000006544009,0.0001609219,0.000005418626,0.0006003007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9405974,"threshold_uncertainty_score":0.9977835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03384231323038684,"score_gpt":0.2553618801002611,"score_spread":0.2215195668698742,"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."}}