{"id":"W4320031295","doi":"10.1109/ssci51031.2022.10022203","title":"Rodent Tracking and Abnormal Behavior Classification in Live Video using Deep Neural Networks","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Neuroendocrine regulation and behavior","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Frame (networking); Artificial neural network; Tracking (education); Opioid; Artificial intelligence; Computer vision; Neuroscience; Audiology; Medicine; Psychology; Internal medicine","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002294986,0.0002639687,0.0002444091,0.0003417061,0.0004808055,0.0001128063,0.0002768463,0.00005371961,0.001415052],"category_scores_gemma":[0.00001111131,0.0003100356,0.00009052881,0.0005326631,0.0001473448,0.0003244172,0.0001225172,0.0005585504,0.00002173731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002329578,"about_ca_system_score_gemma":0.00004226412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001262483,"about_ca_topic_score_gemma":0.00008521517,"domain_scores_codex":[0.9974433,0.000367281,0.0006166485,0.0006579407,0.0005053786,0.000409458],"domain_scores_gemma":[0.9990768,0.0002026676,0.0002413838,0.0002441619,0.000101767,0.0001332014],"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":[0.0002962289,0.0004892831,0.02213245,0.000007908819,0.00001596644,0.00019821,0.001653305,0.953169,0.001316202,0.003913739,0.00009383702,0.01671391],"study_design_scores_gemma":[0.0003474514,0.0005992724,0.1517467,0.0000118377,0.00005254329,0.0008633944,0.0036397,0.8414099,0.000290215,0.0002745633,0.0003185673,0.0004458316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845471,0.0001313763,0.0107367,0.0011966,0.002462022,0.0006462654,0.00003205623,0.00009030581,0.0001575223],"genre_scores_gemma":[0.9981182,0.000009430419,0.0003823766,0.0004464634,0.0001587545,0.0002900756,0.0000950571,0.00004182685,0.0004578453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1296143,"threshold_uncertainty_score":0.9999352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05898141416514661,"score_gpt":0.3398692318174953,"score_spread":0.2808878176523487,"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."}}