{"id":"W3025595575","doi":"10.1523/eneuro.0096-20.2020","title":"Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks","year":2020,"lang":"en","type":"article","venue":"eNeuro","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Health Canada; Canadian Open Neuroscience Platform; Fondation Brain Canada; Fondation Leducq; Government of Canada","keywords":"Computer science; Latency (audio); Tracking (education); Movement (music); Artificial neural network; Brain–computer interface; Real-time computing; Artificial intelligence; Computer hardware; Neuroscience; Psychology","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":[0.0002271072,0.0004521794,0.0002343558,0.0002230832,0.0001096702,0.0002623181,0.0005258675,0.0003690135,0.0009335504],"category_scores_gemma":[0.0005095874,0.0001877494,0.0002381579,0.000142493,0.0001775043,0.0002904548,0.0004234672,0.000454685,0.0002951118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003646923,"about_ca_system_score_gemma":0.0003509935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001827708,"about_ca_topic_score_gemma":0.005128021,"domain_scores_codex":[0.9998957,0.000008893996,0.000004302488,0.00004448334,0.00003072623,0.00001587139],"domain_scores_gemma":[0.9998651,0.00003524463,0.00003316691,0.00002075754,0.00002192335,0.00002383673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002704604,0.00007831478,0.003189401,0.0001328552,0.00006641414,0.0001012273,0.00007766733,0.0204933,0.8129848,0.0007150469,0.001738096,0.1601523],"study_design_scores_gemma":[0.00004458949,0.0003142119,0.01665634,0.00002987679,0.00006188791,0.0002541121,0.00002328139,0.5720237,0.4029724,0.00198303,0.005582036,0.00005455675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2135706,0.000303717,0.7772961,0.00016758,0.00009945995,0.00007570795,0.0009651695,0.00569933,0.00182232],"genre_scores_gemma":[0.6419869,0.0003287989,0.3513811,0.0001951065,0.00002000092,0.0002042993,0.001228973,0.0004335589,0.004221228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001827708,"threshold_uncertainty_score":0.003634155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03948897347568713,"score_gpt":0.2669055191260716,"score_spread":0.2274165456503844,"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."}}