{"id":"W1984963267","doi":"10.1021/ie051031b","title":"Building Multivariate Models from Compressed Data","year":2006,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Multivariate statistics; Compression (physics); Data compression; Wavelet; Interpolation (computer graphics); Context (archaeology); Data set; Principal component analysis; Set (abstract data type); Wavelet transform; Missing data; Data mining; Artificial intelligence; Pattern recognition (psychology); Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001967091,0.000953952,0.0009474015,0.0009952034,0.0002539137,0.001145933,0.000815228,0.0007620738,0.001590207],"category_scores_gemma":[0.009205773,0.0005147625,0.001168961,0.00099258,0.0005665187,0.001671446,0.001021304,0.001558813,0.0004613067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004194607,"about_ca_system_score_gemma":0.0007281261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003349112,"about_ca_topic_score_gemma":0.002367062,"domain_scores_codex":[0.9991115,0.0004041202,0.00004543466,0.0001650664,0.0002125711,0.0000613693],"domain_scores_gemma":[0.9959596,0.0030115,0.0002689483,0.0004594075,0.0002615048,0.00003900293],"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":[0.00009888085,0.00004205094,0.0009910483,0.00009342175,0.0000811249,0.00007798507,0.00007800932,0.9096848,0.0021301,0.009978078,0.0004843076,0.07626016],"study_design_scores_gemma":[0.000002571583,0.00001214509,0.0001199756,0.00000311623,0.000004989462,0.00001290175,0.0000061602,0.9953578,0.0004163397,0.003865235,0.0001941896,0.000004601227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01854541,0.0001172918,0.9801934,0.000140704,0.00001903679,0.00003146906,0.0001756174,0.0004710773,0.0003059404],"genre_scores_gemma":[0.5032192,0.000902459,0.4919703,0.0001357138,0.0001254298,0.0002706447,0.001648565,0.0001681953,0.001559501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003349112,"threshold_uncertainty_score":0.0104031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1405042531671195,"score_gpt":0.3277479907338752,"score_spread":0.1872437375667557,"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."}}