{"id":"W4206924481","doi":"10.1101/2022.01.23.477395","title":"PyRodentTracks: flexible computer vision and RFID based system for multiple rodent tracking and behavioral assessment","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Zebrafish Biomedical Research Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Fondation Brain Canada","keywords":"Computer science; Scalability; Set (abstract data type); Tracking system; Trajectory; Tracking (education); Open source; Similarity (geometry); Real-time computing; Simulation; Artificial intelligence; Computer vision; Human–computer interaction; Software; Operating system; Kalman filter; 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":[0.0007060152,0.0006811169,0.0007310605,0.0008911102,0.0001814968,0.0004783093,0.001796756,0.0006396534,0.008179202],"category_scores_gemma":[0.0008578235,0.0003418956,0.0004366548,0.0003419555,0.0002320362,0.0008351468,0.001142975,0.0006077202,0.003038377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003147886,"about_ca_system_score_gemma":0.000532698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007737008,"about_ca_topic_score_gemma":0.001233289,"domain_scores_codex":[0.9992681,0.00005536394,0.00004832181,0.0002445471,0.000318557,0.00006509142],"domain_scores_gemma":[0.9994363,0.00007256831,0.0001366105,0.0001074005,0.0001686561,0.00007828588],"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":[0.0008238901,0.0002978445,0.00489489,0.0006703452,0.0001041132,0.000405548,0.0001260742,0.001841879,0.6157523,0.001004066,0.02051949,0.3535596],"study_design_scores_gemma":[0.0004278658,0.004856794,0.05034424,0.0003062813,0.0003534901,0.004320177,0.0001097998,0.1195864,0.6511064,0.001560618,0.1665035,0.0005244947],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1058235,0.0008733061,0.8205205,0.0002437882,0.0003310588,0.001097979,0.003913482,0.06122949,0.0059668],"genre_scores_gemma":[0.3764897,0.000875212,0.587952,0.0009097683,0.0001650705,0.002626905,0.006496758,0.001871468,0.02261306],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008179202,"threshold_uncertainty_score":0.02736211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02179390471221279,"score_gpt":0.3089098354939042,"score_spread":0.2871159307816915,"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."}}