{"id":"W2052100537","doi":"10.1504/ijista.2014.059302","title":"Video event detection for fault monitoring in assembly automation","year":2014,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems Technologies and Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Ontario Centres of Excellence","keywords":"Downtime; Testbed; Computer science; Event (particle physics); Real-time computing; Fault detection and isolation; Automation; Fault (geology); Artificial intelligence; Similarity (geometry); Measure (data warehouse); Computer vision; Data mining; Embedded system; Engineering; Image (mathematics); Operating system","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":[0.0005024293,0.0001068929,0.0001636158,0.0004604826,0.00009258026,0.0001697334,0.000783682,0.0001106327,3.434355e-7],"category_scores_gemma":[0.0001086036,0.0000964698,0.00007903727,0.0002738564,0.00003372561,0.000308013,0.0001058134,0.0001512534,0.000002869341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001514322,"about_ca_system_score_gemma":0.00002082143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002227816,"about_ca_topic_score_gemma":0.000005518233,"domain_scores_codex":[0.9987904,0.0000219664,0.0006328278,0.0002014009,0.0002294544,0.000123915],"domain_scores_gemma":[0.9986285,0.0001553522,0.0004807438,0.0002335578,0.0004706838,0.00003118344],"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":[0.000007341965,0.00007978886,0.0003974091,0.00002015027,0.00003250198,5.221443e-7,0.00005586038,0.001694009,0.01034624,0.1396115,0.00006901364,0.8476856],"study_design_scores_gemma":[0.0007637106,0.0005163715,0.002080061,0.0003947839,0.00002794438,0.0003838389,0.002291982,0.3134592,0.3702259,0.05557103,0.2537767,0.0005083653],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01269677,0.0003092763,0.9847015,0.001174523,0.0003446295,0.0004615549,0.000002702871,0.0002370289,0.00007204957],"genre_scores_gemma":[0.9801454,0.0003928931,0.01868517,0.00001273368,0.0001694118,0.0005524742,0.000001082928,0.000007336255,0.00003347774],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9674487,"threshold_uncertainty_score":0.3933924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01561081496717551,"score_gpt":0.2980599717296405,"score_spread":0.2824491567624651,"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."}}