{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003194152,0.0006236507,0.0002698278,0.0008387299,0.0001138494,0.0002348369,0.0004998753,0.0004339021,0.000932269],"category_scores_gemma":[0.0006805196,0.0001452567,0.0002923529,0.0004159168,0.0001294781,0.000291851,0.000291492,0.000364088,0.0003073323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004152253,"about_ca_system_score_gemma":0.0002830882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005843306,"about_ca_topic_score_gemma":0.01087788,"domain_scores_codex":[0.9998386,0.00001987122,0.000007282353,0.00005788734,0.00003881128,0.00003754955],"domain_scores_gemma":[0.9998012,0.00004702582,0.00004378304,0.00002661163,0.00005703609,0.00002428905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001076539,0.0006509785,0.06603453,0.0003820896,0.0003271656,0.0007778282,0.0001718157,0.124245,0.2062331,0.001039956,0.007235412,0.5918255],"study_design_scores_gemma":[0.00001710463,0.000328954,0.04159754,0.00003398614,0.00004695507,0.0002290852,0.00005960857,0.917262,0.03834981,0.0006962728,0.001356976,0.00002179885],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7722109,0.0008048018,0.2151444,0.0002106141,0.0001716178,0.0001378054,0.003097976,0.005245429,0.002976485],"genre_scores_gemma":[0.9167958,0.0004332191,0.07610594,0.0001044636,0.00002885535,0.0001074444,0.003855869,0.00008036579,0.002488019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005843306,"threshold_uncertainty_score":0.01161861,"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."}}