{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001201005,0.00133613,0.0008850616,0.005510152,0.00061719,0.001485775,0.0006245965,0.0005567181,0.002243568],"category_scores_gemma":[0.003846779,0.0004806675,0.001012129,0.006017261,0.0003117479,0.001210514,0.0009552398,0.0009779354,0.0009589421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005950967,"about_ca_system_score_gemma":0.001310055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01004859,"about_ca_topic_score_gemma":0.005910696,"domain_scores_codex":[0.9984214,0.0003169072,0.0001163906,0.0004389607,0.0005758085,0.0001304288],"domain_scores_gemma":[0.9982035,0.0004381136,0.0003584593,0.0002120966,0.0007229203,0.00006500229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003136721,0.0002690115,0.03102196,0.0006490709,0.0005721984,0.0004146853,0.0003776149,0.2783633,0.01675915,0.005571558,0.009510075,0.6561777],"study_design_scores_gemma":[0.00001565171,0.00009032352,0.01669895,0.00004109154,0.00007013858,0.0001036738,0.0001140632,0.9662,0.005563536,0.00538662,0.005647071,0.00006893952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05825307,0.000763977,0.9302487,0.0002396731,0.00009606422,0.0003131957,0.001468592,0.005217147,0.003399599],"genre_scores_gemma":[0.6625066,0.001073987,0.3299683,0.00007194595,0.00009890107,0.0003619835,0.003175948,0.0002503876,0.002492009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01004859,"threshold_uncertainty_score":0.01998019,"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."}}