{"id":"W3126790112","doi":"10.3390/su13041968","title":"Discovering Energy Consumption Patterns with Unsupervised Machine Learning for Canadian In Situ Oil Sands Operations","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tetra Tech (Canada); University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Oil sands; Steam-assisted gravity drainage; Greenhouse gas; Extraction (chemistry); In situ; Steam injection; Petroleum engineering; Environmental science; Energy consumption; Association rule learning; Waste management; Engineering; Computer science; Data mining; Geology; Chemistry; Meteorology; Cartography; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002415997,0.0001319828,0.000163375,0.0001205627,0.0001069554,0.00006774905,0.00006211426,0.00006795699,0.00004624532],"category_scores_gemma":[0.0003012023,0.0001340418,0.00003970014,0.0002041044,0.00001478099,0.0001550786,0.00001395485,0.000159243,3.375103e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007353506,"about_ca_system_score_gemma":0.0002524773,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01261377,"about_ca_topic_score_gemma":0.2562284,"domain_scores_codex":[0.9991408,0.00007700302,0.0001860723,0.0002022803,0.0000814946,0.000312339],"domain_scores_gemma":[0.9993574,0.0001095637,0.000006900998,0.0002103631,0.0001933963,0.0001223901],"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":[0.000008627162,0.000009215551,0.08345807,0.0003095088,0.00001164949,0.00001327404,0.0003191678,0.9124325,0.00026938,0.0003610961,0.000001740143,0.002805796],"study_design_scores_gemma":[0.0007881123,0.00002181435,0.04413748,0.00003160743,0.000008496658,0.000003409133,0.000337593,0.9504131,0.001508764,0.00009823474,0.002432139,0.0002192262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8115,0.0001697868,0.1877622,0.0001515305,0.00005913189,0.0000696116,0.00001515562,0.00009205488,0.0001805251],"genre_scores_gemma":[0.9966133,0.0000485098,0.002687037,0.0000129725,0.00002985007,0.00008755786,0.0001562503,0.00002897288,0.0003355962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2436147,"threshold_uncertainty_score":0.9939613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01066556565531849,"score_gpt":0.2528636188846744,"score_spread":0.2421980532293559,"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."}}