{"id":"W3018127835","doi":"10.1016/j.cageo.2020.104501","title":"Improved well log classification using semisupervised Gaussian mixture models and a new hyper-parameter selection strategy","year":2020,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Chevron","keywords":"Overfitting; Artificial intelligence; Computer science; Machine learning; Context (archaeology); Selection (genetic algorithm); Gaussian process; Model selection; Core (optical fiber); Pattern recognition (psychology); Process (computing); Gaussian; Artificial neural network","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":[],"consensus_categories":[],"category_scores_codex":[0.0001152943,0.0001535462,0.0001564923,0.00007570229,0.0000906613,0.0001842166,0.00016697,0.00008496059,0.000008871241],"category_scores_gemma":[0.00001753328,0.0001430749,0.00003573941,0.0004132829,0.00003797475,0.000382423,0.00002461896,0.0001466618,0.000001756232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000270979,"about_ca_system_score_gemma":0.00003663596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004207304,"about_ca_topic_score_gemma":0.000002453867,"domain_scores_codex":[0.9991426,0.00003491573,0.000181721,0.0002873401,0.0001347471,0.0002186212],"domain_scores_gemma":[0.9995771,0.00006296576,0.00002761461,0.00009969545,0.00002614742,0.0002064474],"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":[0.000003442847,0.000002861068,0.0001588351,0.00003040255,0.000006350362,3.689324e-7,0.0003229484,0.9626783,0.02731873,0.00004487012,0.0001097447,0.009323133],"study_design_scores_gemma":[0.000211602,0.00004693992,0.000660651,0.00001424951,0.000008886709,0.000005124839,0.00005815168,0.99729,0.0009041648,0.0002320752,0.0003989752,0.0001691813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3600545,0.0001698969,0.6391105,0.0001312269,0.0001660184,0.00008157103,9.117078e-7,0.0001826314,0.0001027111],"genre_scores_gemma":[0.855546,0.00002891625,0.1441649,0.00007713946,0.0001389664,0.000001937142,0.000003250407,0.00001385259,0.00002507097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4954915,"threshold_uncertainty_score":0.5834423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0752943942574751,"score_gpt":0.2792089018939876,"score_spread":0.2039145076365125,"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."}}