{"id":"W1971669100","doi":"10.1016/j.envpol.2007.04.010","title":"IPCS: An integrated process control system for enhanced in-situ bioremediation","year":2007,"lang":"en","type":"article","venue":"Environmental Pollution","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of New Brunswick; University of Regina","funders":"Ministry of Science and Technology of the People's Republic of China; Tsinghua University; National Natural Science Foundation of China","keywords":"Bioremediation; Process (computing); Environmental remediation; Computer science; Biochemical engineering; Environmental science; Artificial neural network; Process engineering; Control engineering; Engineering; Artificial intelligence; Ecology; Contamination; Biology","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":[0.0004178324,0.0001192813,0.0001219007,0.0001259053,0.00003264024,0.00001194782,0.00006144291,0.00009942571,0.000005187221],"category_scores_gemma":[0.00001610971,0.0001237682,0.00003086554,0.0001029217,0.00001502385,0.0002031646,0.000002698094,0.00008547752,0.00000878244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003875623,"about_ca_system_score_gemma":0.000003752464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003086269,"about_ca_topic_score_gemma":0.00001101011,"domain_scores_codex":[0.9992631,0.00002296343,0.0002443512,0.000137421,0.000120532,0.000211628],"domain_scores_gemma":[0.9997636,0.00003307568,0.00002872057,0.0001058074,0.000004251859,0.00006455677],"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":[0.00003467992,0.00001644634,0.0002446406,0.00003745603,0.000006043195,5.002631e-7,0.0001556505,0.6535863,0.3403036,0.00001986695,0.000001934593,0.0055929],"study_design_scores_gemma":[0.001889148,0.00007220004,0.03515072,0.00003503162,0.00001066158,0.000001569336,0.0007260601,0.5956212,0.3660229,0.0000263324,0.0002289461,0.0002152402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5369924,0.00004131296,0.4623435,0.000003563497,0.0001693429,0.0002087492,0.00001195682,0.0001246759,0.0001043963],"genre_scores_gemma":[0.9961382,0.000005334804,0.003566191,0.000007762548,0.0001093281,0.00004083621,0.00009179072,0.00002623092,0.00001437321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4591457,"threshold_uncertainty_score":0.504712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006897944950372414,"score_gpt":0.241141711432024,"score_spread":0.2342437664816516,"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."}}