{"id":"W2128907450","doi":"10.1002/aic.690480507","title":"Cycle detection and characterization in chemical engineering","year":2002,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistic; Series (stratigraphy); Biological system; Flow (mathematics); Amplitude; Characterization (materials science); Process engineering; Algorithm; Computer science; Mechanics; Mathematics; Materials science; Engineering; Statistics; Physics; Nanotechnology","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":[0.0001507968,0.00005002839,0.0001378701,0.0001467642,0.00003439247,0.00006189552,0.00003635515,0.00003548644,0.0004367513],"category_scores_gemma":[0.00002541562,0.00005713558,0.00003455803,0.0001478458,0.000005072603,0.0001685352,0.00001305646,0.0001124157,0.00004887146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004102083,"about_ca_system_score_gemma":6.721338e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002296403,"about_ca_topic_score_gemma":0.00000431738,"domain_scores_codex":[0.999516,0.000003646321,0.0002730248,0.00008991794,0.00001556366,0.0001018413],"domain_scores_gemma":[0.9997832,0.000007016783,0.0001077318,0.00005367334,0.000008193975,0.00004019004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006592007,0.0005328018,0.4130184,0.0002102025,0.0005639123,0.0001049494,0.006441986,0.002629983,0.3486377,0.04134511,0.0004517306,0.1859972],"study_design_scores_gemma":[0.00111804,0.00006908798,0.2839257,0.00003708657,0.00001254892,0.0003333863,0.00005296979,0.6554845,0.001306133,0.002837467,0.05437544,0.000447627],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943033,0.0005902083,0.004081691,0.0002433469,0.0001080138,0.00002388458,0.000003953048,0.000007931343,0.0006376802],"genre_scores_gemma":[0.9994317,0.0001427628,0.0001119332,0.00002915926,0.0001339633,0.000001524113,0.000001053437,0.000006780459,0.0001411173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6528545,"threshold_uncertainty_score":0.4782121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01367831571497581,"score_gpt":0.1610201532331332,"score_spread":0.1473418375181574,"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."}}