{"id":"W2170336234","doi":"10.1002/cjce.21802","title":"A developed smart technique to predict minimum miscible pressure—eor implications","year":2013,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Waterloo","funders":"","keywords":"Petroleum engineering; Particle swarm optimization; Miscibility; Artificial neural network; Enhanced oil recovery; Engineering; Computer science; Mathematics; Chemistry; Mathematical optimization; Machine learning; Polymer","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000243097,0.0004871241,0.0003114399,0.0003149138,0.0001501967,0.0003125744,0.0004373511,0.0006100641,0.0008214485],"category_scores_gemma":[0.0006149354,0.0002605148,0.0003202818,0.0001850309,0.0001849397,0.0005218086,0.0002883298,0.000424979,0.0001714198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002725488,"about_ca_system_score_gemma":0.000343504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003647683,"about_ca_topic_score_gemma":0.00280885,"domain_scores_codex":[0.9999117,0.00001636226,0.00000572808,0.00002352137,0.000033417,0.000009371078],"domain_scores_gemma":[0.9998519,0.00005966198,0.00002544206,0.00001420965,0.0000431316,0.000005666421],"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":[0.00004598709,0.00003611414,0.001992814,0.00004169368,0.00001898826,0.00007147688,0.0000253029,0.948483,0.01315986,0.0007770126,0.0002257392,0.03512188],"study_design_scores_gemma":[8.853058e-7,0.000008657999,0.0001760327,0.00000119765,0.000001667612,0.00000536539,0.000001261321,0.9985091,0.001154507,0.00007337183,0.00006658644,0.000001474052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1674089,0.0002336945,0.8265629,0.000168891,0.00005483447,0.00005587072,0.0001149679,0.0008208624,0.004578921],"genre_scores_gemma":[0.9598268,0.0000866046,0.03863071,0.0000243103,0.00000723417,0.00003838906,0.00005318688,0.00001578823,0.001316971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003647683,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007604620813164978,"score_gpt":0.1970724372458656,"score_spread":0.1894678164327006,"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."}}