{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008205744,0.0003977355,0.0003864439,0.0001647089,0.0003425792,0.0002929424,0.0004260198,0.000391194,0.00001389038],"category_scores_gemma":[0.00007836596,0.0004225398,0.0001238603,0.0001602875,0.0001817008,0.00001220901,0.001088373,0.0004641,0.000001474284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002131741,"about_ca_system_score_gemma":0.0004855629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004725911,"about_ca_topic_score_gemma":0.000006250329,"domain_scores_codex":[0.9972431,0.000138408,0.0004292413,0.00123646,0.0004600945,0.0004926985],"domain_scores_gemma":[0.9980901,0.00007439072,0.0002380239,0.0008914391,0.0003146894,0.0003913321],"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":[0.00008285121,0.0003084263,0.009291136,0.0007736462,0.00008523537,0.00001190183,0.000002612822,0.0001179609,0.9886009,0.0001267144,0.000539224,0.0000594107],"study_design_scores_gemma":[0.00409947,0.00154287,0.2673591,0.000586462,0.0002818866,2.255003e-7,0.00002790349,0.03599512,0.6445401,0.000002456315,0.04388271,0.001681721],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9216672,0.0008307375,0.0739576,0.0002698983,0.0003846709,0.00185784,0.000899703,0.0001299855,0.000002377695],"genre_scores_gemma":[0.9722052,0.0001186913,0.02569351,0.0001129373,0.0003480537,0.001396735,0.00002172638,0.00009901702,0.000004162571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3440608,"threshold_uncertainty_score":0.9998226,"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."}}