{"id":"W4388994712","doi":"10.1016/j.jprocont.2023.103127","title":"Visual analytics for process monitoring: Leveraging time-series imaging for enhanced interpretability","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Visual analytics; Computer science; Analytics; Process (computing); Machine learning; Artificial intelligence; Scalability; Visualization; Leverage (statistics); Interactive visual analysis; Big data; Convolutional neural network; Benchmark (surveying); Data science; Data mining","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":[0.0005546059,0.0007627056,0.0004772744,0.001100663,0.0001905753,0.001516476,0.000627799,0.0006158348,0.003131373],"category_scores_gemma":[0.002238435,0.000311036,0.0003779969,0.0006250458,0.0003789297,0.00145371,0.0009175989,0.0009127368,0.0006077178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002799381,"about_ca_system_score_gemma":0.0003053781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008429359,"about_ca_topic_score_gemma":0.001065336,"domain_scores_codex":[0.9997239,0.00004424817,0.00001578275,0.00005825011,0.0001282367,0.00002968464],"domain_scores_gemma":[0.9990572,0.0004614397,0.0001129812,0.0001296411,0.0001870608,0.00005153162],"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":[0.0005051558,0.000210263,0.00144456,0.0003987778,0.00006509446,0.0002444494,0.0002149903,0.02830069,0.5810387,0.00571011,0.003632858,0.3782344],"study_design_scores_gemma":[0.0000220778,0.0001463476,0.00220754,0.00004418899,0.00003844746,0.0003373529,0.00008646984,0.7969031,0.1848388,0.009370681,0.005953337,0.00005176998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0674868,0.0009400227,0.9220008,0.0006494952,0.0001852512,0.00007089289,0.0004212272,0.004257719,0.003987824],"genre_scores_gemma":[0.5991626,0.001403313,0.3950285,0.0003575817,0.0002045934,0.00007456157,0.0004861828,0.0007013205,0.002581327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003131373,"threshold_uncertainty_score":0.01047546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01030583001426595,"score_gpt":0.2962777609987008,"score_spread":0.2859719309844349,"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."}}