{"id":"W7030149366","doi":"","title":"Mining infrequent group of motifs from multidimensional time series: A case study at Alfa Laval AB","year":2024,"lang":"en","type":"article","venue":"Hogskolan Ihalmstad (Halmstad University)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pairwise comparison; Similarity (geometry); Structural motif; Pattern recognition (psychology); Matrix (chemical analysis); Bridging (networking); Group (periodic table)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003878071,0.0004180046,0.0005852909,0.0007013704,0.0004256078,0.0001822527,0.0007826491,0.0001386918,0.0003253939],"category_scores_gemma":[0.00003274606,0.0004233305,0.0003334079,0.001580865,0.0001551782,0.001297698,0.001247767,0.0002738358,0.0001097605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003128883,"about_ca_system_score_gemma":0.0001569032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003671204,"about_ca_topic_score_gemma":0.001075773,"domain_scores_codex":[0.9970262,0.0002432389,0.0005195222,0.001065377,0.0006026896,0.0005429421],"domain_scores_gemma":[0.9981204,0.000322228,0.0002286827,0.0008756432,0.0001712919,0.0002816997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"case_report","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001498612,0.005553165,0.07942978,0.0007519385,0.00940196,0.513085,0.1477511,0.007144282,0.06442276,0.09616023,0.01091648,0.06388468],"study_design_scores_gemma":[0.009892731,0.006700749,0.01149687,0.001527065,0.002571662,0.01173755,0.08247231,0.6718533,0.004957912,0.0007760049,0.1901227,0.005891193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875053,0.0004201757,0.007998432,0.00009548928,0.0003190856,0.0003175843,0.0001195089,0.0003528401,0.00287158],"genre_scores_gemma":[0.9797512,0.00003450487,0.01465956,0.00001748428,0.00009675001,0.000001688939,0.0000493911,0.00003874493,0.005350647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.664709,"threshold_uncertainty_score":0.9998218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01132290235024775,"score_gpt":0.19759105099953,"score_spread":0.1862681486492823,"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."}}