{"id":"W4408469642","doi":"10.1016/j.compeleceng.2025.110251","title":"AI-DeepFrothNet: Continuous monitoring and tracking of froth flotation working condition by root cause analysis and optimized predictive control","year":2025,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Minerals Flotation and Separation Techniques","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Moncton","funders":"National Natural Science Foundation of China","keywords":"Model predictive control; Root cause analysis; Tracking (education); Root (linguistics); Control (management); Computer science; Control theory (sociology); Engineering; Environmental science; Artificial intelligence; Forensic engineering; Psychology; Philosophy","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":[0.0001272048,0.000121668,0.000270846,0.0001901564,0.00005814349,0.0000518923,0.00006047801,0.00006135224,0.00001095702],"category_scores_gemma":[0.00004176332,0.0001293455,0.000041444,0.0006035475,0.00003382201,0.0001530275,0.00002489942,0.0001157507,3.379469e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007429956,"about_ca_system_score_gemma":0.000004343789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005095579,"about_ca_topic_score_gemma":0.000001924843,"domain_scores_codex":[0.9991921,0.00003732457,0.0002599201,0.0002306086,0.0001307578,0.000149251],"domain_scores_gemma":[0.9996023,0.0001721402,0.00007523909,0.00007480203,0.00001820193,0.00005737026],"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":[0.000137689,0.00009833727,0.1417199,0.00003412439,0.0006974777,0.000004479117,0.0007299704,0.4287004,0.3807037,0.0004249393,0.001009971,0.045739],"study_design_scores_gemma":[0.0007374722,0.00006005632,0.07194386,0.00003224884,0.0001471582,9.848019e-7,0.000005910596,0.9032677,0.02354607,0.00004217254,0.00009806891,0.0001183074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2729101,0.0002156812,0.7264705,0.00005384134,0.00003829696,0.0001848455,0.000002214453,0.00008370825,0.00004088648],"genre_scores_gemma":[0.993735,0.00003634205,0.0060885,0.00005797848,0.00001490074,0.00002663035,0.0000110273,0.000007474384,0.00002211578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.720825,"threshold_uncertainty_score":0.5274556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003596336362239905,"score_gpt":0.2259090827100393,"score_spread":0.2223127463477994,"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."}}