{"id":"W2035390569","doi":"10.3182/20090630-4-es-2003.00209","title":"Qualitative representation of trends (QRT) as a tool for automated data-driven monitoring of on-line sensors","year":2009,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Fault detection and isolation; Fault (geology); Line (geometry); Representation (politics); Computer science; Real-time computing; Data mining; Spectrum analyzer; Engineering; Control engineering; Reliability engineering; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002062889,0.0007840845,0.0004962925,0.002603871,0.0003016373,0.001775676,0.0009583457,0.0005265328,0.006942096],"category_scores_gemma":[0.006519981,0.0003387195,0.0007679606,0.001821314,0.0006647878,0.001816472,0.0009858237,0.0007348565,0.001125287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005753159,"about_ca_system_score_gemma":0.0008405001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002892866,"about_ca_topic_score_gemma":0.002084847,"domain_scores_codex":[0.9989931,0.0002784104,0.0001251976,0.0001470393,0.0003963363,0.00005998152],"domain_scores_gemma":[0.995462,0.002232354,0.0007374846,0.0005285583,0.0008917855,0.0001477882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009024247,0.0002120937,0.01099818,0.001956447,0.0001840097,0.0009591458,0.00295246,0.2108746,0.07133699,0.2697131,0.0163695,0.413541],"study_design_scores_gemma":[0.00006189822,0.0002303914,0.002092995,0.0001901582,0.0001041943,0.0002996929,0.0004300672,0.8516586,0.0318411,0.07794423,0.03505592,0.0000908093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005249775,0.00005300477,0.9872254,0.00007580255,0.00003414014,0.00006628018,0.00122717,0.004801462,0.001267153],"genre_scores_gemma":[0.290537,0.0002345174,0.7035381,0.0001054388,0.00004623416,0.0004744764,0.002406606,0.0007645949,0.001893017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006942096,"threshold_uncertainty_score":0.02322364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05072478546688537,"score_gpt":0.3662908367797416,"score_spread":0.3155660513128562,"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."}}