{"id":"W4402299007","doi":"10.1016/j.ifacol.2024.08.378","title":"Reservoir computing-based slow feature analysis: Application in fault classification","year":2024,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Feature (linguistics); Computer science; Fault (geology); Pattern recognition (psychology); Artificial intelligence; Data mining; Geology; Seismology","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":[],"consensus_categories":[],"category_scores_codex":[0.0005483478,0.0002575855,0.0003234366,0.0005245898,0.0001650418,0.0004054269,0.001017973,0.0001812904,0.000005005374],"category_scores_gemma":[0.00003289058,0.00021547,0.0002317069,0.004325732,0.00004447754,0.0003060673,0.0001909762,0.0005688671,0.00004510938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001258836,"about_ca_system_score_gemma":0.0001001177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000889143,"about_ca_topic_score_gemma":0.0003011345,"domain_scores_codex":[0.9976315,0.0001411874,0.0003939162,0.0009366046,0.0004434429,0.0004533853],"domain_scores_gemma":[0.9986116,0.0002692112,0.000114506,0.0007825839,0.00009917203,0.0001229188],"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":[0.00003112185,0.0002860169,0.007838582,0.0002157016,0.0002584417,0.0001569213,0.0008037578,0.8260448,0.005842177,0.008129391,0.0004700759,0.149923],"study_design_scores_gemma":[0.0002201491,0.00003760829,0.01372323,0.0001069358,0.0000432628,0.000004926054,0.00003651429,0.98258,0.0001152588,0.000182238,0.002702799,0.0002470686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1302035,0.001667249,0.8408357,0.02519827,0.0005329725,0.0004058684,0.000009272807,0.0007295292,0.0004177111],"genre_scores_gemma":[0.8044896,0.00002039894,0.1942056,0.0005072412,0.0003450635,0.0000157519,0.0001041802,0.00001838966,0.0002937815],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6742861,"threshold_uncertainty_score":0.8786613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01683661508015006,"score_gpt":0.2825776065403499,"score_spread":0.2657409914601999,"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."}}