{"id":"W4387800258","doi":"10.48550/arxiv.2310.11475","title":"Tracking and Mapping in Medical Computer Vision: A Review","year":2023,"lang":"en","type":"review","venue":"arXiv (Cornell University)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Field (mathematics); Process (computing); Computer vision; Artificial intelligence; Tracking (education); Data science; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002562227,0.0002292901,0.0007864323,0.0002641465,0.00003286322,0.00002231688,0.0001897528,0.0002376975,0.00003701064],"category_scores_gemma":[0.00002658181,0.0002357375,0.0001351546,0.0009410677,0.00003191806,0.00007841716,0.00007720661,0.0003312878,0.00006872417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009717092,"about_ca_system_score_gemma":0.00004086931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006209353,"about_ca_topic_score_gemma":0.00001401669,"domain_scores_codex":[0.9989997,0.00008554317,0.0003082027,0.0003311177,0.00007564987,0.0001997742],"domain_scores_gemma":[0.999458,0.0001434812,0.00005653737,0.0002058755,0.00002088543,0.0001152241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[7.985792e-7,0.0000205812,0.00001986526,0.1047417,0.0001194966,0.002154793,0.00002689778,0.05926462,3.402355e-8,0.003412474,0.001194808,0.8290439],"study_design_scores_gemma":[0.0001577942,0.00001170821,0.00001054022,0.1012427,0.0001567152,0.00002858932,0.000004718325,0.4965324,1.446382e-8,0.00006854418,0.4014435,0.0003428135],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002058141,0.9574516,0.04167132,0.00001236207,0.0001831428,0.0002858406,0.000003320574,0.0001699617,0.0002018071],"genre_scores_gemma":[0.0002509627,0.9994269,0.0000604315,0.00004780787,0.00007077726,5.693136e-7,0.00003312516,0.00004424036,0.00006517258],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8287011,"threshold_uncertainty_score":0.9613096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.119841938784801,"score_gpt":0.2320985693655895,"score_spread":0.1122566305807884,"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."}}