{"id":"W2151941337","doi":"10.1142/s0219878908001521","title":"FAULT DIAGNOSIS OF AN INDUSTRIAL MACHINE THROUGH SENSOR FUSION","year":2008,"lang":"en","type":"article","venue":"International Journal of Information Acquisition","topic":"Industrial Technology and Control Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Artificial neural network; Robustness (evolution); Fault (geology); Accelerometer; Microphone; Fast Fourier transform; Feature vector; Feature (linguistics); Vibration; Fuzzy logic; Fault detection and isolation; Pattern recognition (psychology); Algorithm; Acoustics","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":[0.0002390233,0.0000922918,0.0001757327,0.0002984225,0.00004327245,0.00002108642,0.0002412865,0.0002058447,0.0001773026],"category_scores_gemma":[0.00008758044,0.00008393031,0.00008662602,0.0001195062,0.00003534606,0.002087626,0.00001580898,0.000244485,0.00002174539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001053217,"about_ca_system_score_gemma":0.00003290452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002585244,"about_ca_topic_score_gemma":0.000001164624,"domain_scores_codex":[0.998622,0.00003351093,0.0007940765,0.0000335417,0.0004309957,0.00008584779],"domain_scores_gemma":[0.998885,0.00004021292,0.0004462659,0.00007972056,0.0005148079,0.00003398502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004924146,0.0009486239,0.03121779,0.0001381232,0.002572485,0.0003800221,0.01397713,0.3133888,0.02609733,0.01627651,0.0462109,0.5438681],"study_design_scores_gemma":[0.04656946,0.003686419,0.02638267,0.001746665,0.0003118866,0.01042822,0.004790149,0.1792745,0.4769014,0.004953379,0.2429951,0.001960186],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819932,0.0000809327,0.01272257,0.0003358025,0.002444968,0.0001228422,0.00006446569,0.00006992729,0.002165321],"genre_scores_gemma":[0.9984722,0.0001568086,0.0006060315,0.000122051,0.0005622371,0.000004980371,0.00006198603,0.000005898091,0.000007823971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.541908,"threshold_uncertainty_score":0.3422579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01695096198053401,"score_gpt":0.2373228495620291,"score_spread":0.2203718875814951,"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."}}