{"id":"W4214832602","doi":"10.1155/2022/5631281","title":"Intelligent Transport Surveillance Memory Enhanced Method for Detection of Abnormal Behavior in Video","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":true,"has_abstract":true,"ca_institutions":"","funders":"Zhejiang Shuren University; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Autoencoder; Artificial intelligence; Convolutional neural network; Coding (social sciences); Process (computing); Pattern recognition (psychology); Computer vision; Artificial neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":{"nature":"Retraction","reason":"Concerns/Issues about Data;Concerns/Issues about Results and/or Conclusions;Concerns/Issues about Referencing/Attributions;Concerns/Issues about Peer Review;Investigation by Journal/Publisher;Investigation by Third Party;Paper Mill;Computer-Aided Content or Computer-Generated Content;Unreliable Results and/or Conclusions;","date":"8/9/2023 0:00","openalex_flagged":true},"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003543823,0.0005497545,0.0004188636,0.0008630728,0.0001698256,0.0003766989,0.0006342406,0.0003879642,0.001018495],"category_scores_gemma":[0.0008186201,0.0001782735,0.0005460323,0.0003916779,0.0001931822,0.0008097659,0.0004366052,0.0005388198,0.0002722034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004133324,"about_ca_system_score_gemma":0.0004916108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00409243,"about_ca_topic_score_gemma":0.004395217,"domain_scores_codex":[0.9997861,0.00002383928,0.00001634903,0.0000600384,0.00008468387,0.00002904541],"domain_scores_gemma":[0.9997506,0.00003848893,0.00003128453,0.00003763881,0.0001290499,0.00001296853],"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":[0.0003846803,0.0001513236,0.00334032,0.0001475491,0.0001065301,0.0002032365,0.0001268045,0.04069205,0.1296329,0.002113055,0.002462148,0.8206393],"study_design_scores_gemma":[0.00001687295,0.0001561596,0.003775109,0.00001838163,0.00007798338,0.0002982987,0.00004184329,0.9074228,0.08507605,0.0006359576,0.002460407,0.00002029025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1298142,0.001067477,0.8640746,0.0001743244,0.0001138385,0.00010692,0.0001771972,0.001986903,0.002484495],"genre_scores_gemma":[0.6797044,0.0009664894,0.3121822,0.00018619,0.00008476029,0.0000978274,0.0006113781,0.0001181418,0.006048647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00409243,"threshold_uncertainty_score":0.008137226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0115335874675299,"score_gpt":0.2908274838851406,"score_spread":0.2792938964176107,"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."}}