{"id":"W3037925704","doi":"10.1021/acs.iecr.0c01038","title":"Supply Chain Monitoring Using Principal Component Analysis","year":2020,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"ProSensus (Canada); McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Principal component analysis; Component (thermodynamics); Supply chain; Computer science; Process engineering; Reliability engineering; Chemistry; Business; Artificial intelligence; Thermodynamics; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005473227,0.0002477706,0.0004085336,0.0001849289,0.0001078167,0.0001500406,0.0003637477,0.0003027973,0.0001120903],"category_scores_gemma":[0.0002420036,0.0002842467,0.0001809076,0.001864796,0.00003080799,0.00009007588,0.00009143688,0.00127656,0.00002761149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003616295,"about_ca_system_score_gemma":0.0000523981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009047863,"about_ca_topic_score_gemma":5.412458e-7,"domain_scores_codex":[0.9977922,0.00004559947,0.0004120604,0.0003468503,0.0007167934,0.0006865064],"domain_scores_gemma":[0.9990098,0.0001191033,0.00002717699,0.0002994142,0.00008930681,0.0004551916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001473608,0.000006109939,0.0009961361,0.00006245009,0.0002678105,0.00001914791,0.00008316129,0.4222215,0.5759966,0.000001631675,0.00006659862,0.0002640629],"study_design_scores_gemma":[0.0005231475,0.00001343358,0.0001260876,0.00004018925,0.00004796277,0.00000505836,0.0001168582,0.6699815,0.3234299,2.955543e-7,0.005500185,0.0002152907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973108,0.0002460649,0.0005331042,0.0001150542,0.0004222516,0.0002354179,0.00002116978,0.0006129738,0.0005031383],"genre_scores_gemma":[0.9972132,0.00001094707,0.00003650663,0.000002368449,0.002541298,0.00004141511,0.00001149324,0.00005912206,0.00008362797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2525667,"threshold_uncertainty_score":0.999961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1165232034229518,"score_gpt":0.3222188825105345,"score_spread":0.2056956790875827,"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."}}