{"id":"W3017631812","doi":"10.3791/61012","title":"Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)","year":2020,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Neurobiology and Insect Physiology Research","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Institute of Molecular and Cell Biology; National Medical Research Council; Medical Research Council; Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Artificial intelligence; Segmentation; Computer science; Gait; Computer vision; Tracking (education); Feature (linguistics); Pattern recognition (psychology); Biology","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":[0.0004016086,0.0002139984,0.000428408,0.000271004,0.0002664595,0.0001206688,0.0002440483,0.0001584661,0.00003734618],"category_scores_gemma":[0.0008552673,0.0001957342,0.00008601222,0.0004447781,0.0001288253,0.0008985513,0.0001093307,0.0009434573,0.000003787927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009023979,"about_ca_system_score_gemma":0.00009801463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000120565,"about_ca_topic_score_gemma":6.749913e-7,"domain_scores_codex":[0.9974288,0.0009530775,0.000543276,0.0003596269,0.0003189215,0.00039632],"domain_scores_gemma":[0.9989327,0.0002630153,0.0004823234,0.00007733602,0.00008157299,0.0001630333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005904227,0.00009697018,0.0053342,0.00003139252,0.0000180051,0.0002359344,0.00444837,0.0006753635,0.9879763,0.00002074545,0.00004340468,0.0005288583],"study_design_scores_gemma":[0.002505151,0.0007221349,0.007466502,0.0001446468,0.00001251333,0.0002314106,0.0008758095,0.01648089,0.9712918,0.00002755812,0.00005830568,0.0001832582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986596,0.0004180477,0.0001950938,0.0001982251,0.000208299,0.0001922698,0.000001525804,0.00006849867,0.00005840823],"genre_scores_gemma":[0.9982739,0.0001015295,0.0006004382,0.0008488257,0.0001265001,0.00000313432,0.000001658319,0.0000299982,0.00001398699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01668451,"threshold_uncertainty_score":0.7981809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09626225486556854,"score_gpt":0.4556428833319082,"score_spread":0.3593806284663396,"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."}}