{"id":"W1985986137","doi":"10.1016/j.measurement.2010.02.014","title":"Support vector machine based data processing algorithm for wear degree classification of slurry pump systems","year":2010,"lang":"en","type":"article","venue":"Measurement","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Feature selection; Support vector machine; Outlier; Data mining; Fault (geology); Computer science; Algorithm; Feature (linguistics); Statistical classification; Selection (genetic algorithm); Pattern recognition (psychology); Artificial intelligence","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":[0.001068891,0.0001368349,0.0002091873,0.00007607455,0.00006723582,0.00004887126,0.0002648828,0.00008455564,0.00002095635],"category_scores_gemma":[0.00006094247,0.0001267712,0.0000428296,0.0001024634,0.00001590864,0.0001135954,0.00001236437,0.000131294,0.00001116206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007775483,"about_ca_system_score_gemma":0.00007611922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005672226,"about_ca_topic_score_gemma":0.0001650122,"domain_scores_codex":[0.9987214,0.00002566113,0.0003772878,0.000216691,0.0004743077,0.00018472],"domain_scores_gemma":[0.9990658,0.00001566489,0.00009540637,0.0005494972,0.0002029883,0.00007061059],"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":[0.00002839485,0.0001275062,0.000248865,0.001011451,0.00009846757,9.295934e-7,0.00008487221,0.000961833,0.7698805,0.00003429415,0.001687336,0.2258356],"study_design_scores_gemma":[0.0007275483,0.00005051414,0.0009583645,0.00005481327,0.00003606888,0.000003041859,0.00005057251,0.9357777,0.008932773,0.000001228958,0.05327456,0.0001328064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007958712,0.0009310213,0.9825292,0.0001542607,0.004295765,0.001848006,0.0003677509,0.0005391458,0.001376182],"genre_scores_gemma":[0.997604,0.000001361843,0.001794254,0.000009080513,0.0002454869,0.0001776716,0.00007595262,0.00003564099,0.00005658863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9896452,"threshold_uncertainty_score":0.5169579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.086666520177986,"score_gpt":0.2647865828598872,"score_spread":0.1781200626819012,"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."}}