{"id":"W3045231911","doi":"10.1109/iccicc46617.2019.9146048","title":"Sparse spatiotemporal feature learning for pipeline anomaly detection","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline (software); Anomaly detection; Computer science; Feature (linguistics); Noise (video); Embedding; Pipeline transport; Data mining; Pattern recognition (psychology); Anomaly (physics); Feature vector; Artificial intelligence; Machine learning; Engineering; Image (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":[0.0005184979,0.0004673898,0.0006481706,0.0006139246,0.0002028008,0.0004325662,0.0006473048,0.0006014096,0.0007998652],"category_scores_gemma":[0.002885945,0.0002645567,0.0004398171,0.00105992,0.0003532661,0.0009627243,0.0006405321,0.001020059,0.0002646021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005252323,"about_ca_system_score_gemma":0.0006087152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006668675,"about_ca_topic_score_gemma":0.004541337,"domain_scores_codex":[0.9996797,0.00007244504,0.00002234436,0.000100046,0.00008752468,0.00003804246],"domain_scores_gemma":[0.9989221,0.0005971515,0.0001474236,0.0001184063,0.0001844834,0.00003046441],"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":[0.000139135,0.00009710611,0.001896442,0.00008749744,0.0000628423,0.0001043497,0.00006964484,0.7025016,0.01343449,0.008949555,0.002832117,0.2698252],"study_design_scores_gemma":[0.000001338704,0.000008956166,0.0002044982,0.000001340887,0.000002219514,0.00001045672,0.000002725419,0.9969337,0.000626798,0.00201157,0.0001936436,0.000002751789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01706886,0.0002177001,0.9815115,0.000132037,0.00001916624,0.00001275521,0.00017873,0.0005493006,0.0003098164],"genre_scores_gemma":[0.7705818,0.0005188597,0.2253699,0.0001016205,0.0001095322,0.00008455293,0.001299427,0.00009281701,0.001841576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006668675,"threshold_uncertainty_score":0.01325977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01040756448273103,"score_gpt":0.2403134888220055,"score_spread":0.2299059243392745,"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."}}