{"id":"W7134844736","doi":"10.66238/ijcbs22","title":"Lightweight Markerless AR Registration Using Depth Information for Endoscopic Procedure","year":2025,"lang":"","type":"article","venue":"International Journal of Computational and Biological Sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Frame (networking); Cut; Graph; Image registration; Sampling (signal processing); Mean squared error; Point cloud","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004305957,0.0009531371,0.0006739869,0.000771563,0.0002715427,0.0009784997,0.001195174,0.0006903221,0.00473215],"category_scores_gemma":[0.002446427,0.00069723,0.0006444879,0.0008870664,0.0003446967,0.001494067,0.001747536,0.0009558266,0.002465458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002826478,"about_ca_system_score_gemma":0.0007726181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001543153,"about_ca_topic_score_gemma":0.002840573,"domain_scores_codex":[0.9990197,0.0001465835,0.00004188671,0.0001592609,0.0005544276,0.00007811256],"domain_scores_gemma":[0.9992674,0.0001952947,0.00009922362,0.0002669189,0.0001415433,0.00002962901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006043926,0.0001383541,0.001585084,0.0003163488,0.0001138579,0.0002894801,0.0001900553,0.04390733,0.2804972,0.005816692,0.007475194,0.6590661],"study_design_scores_gemma":[0.0001175985,0.0005442457,0.005591096,0.00006161018,0.0001051799,0.002030691,0.0001083491,0.7456384,0.2078179,0.008662698,0.02920519,0.0001170171],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01287714,0.0002417322,0.9823998,0.00009860302,0.00005276269,0.00004333937,0.0001155432,0.00279962,0.001371409],"genre_scores_gemma":[0.3246381,0.0004139614,0.6700928,0.0001928793,0.00008531423,0.0001412264,0.0006934004,0.0007943516,0.002948013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00473215,"threshold_uncertainty_score":0.01583064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03051146345320197,"score_gpt":0.2977613447234012,"score_spread":0.2672498812701992,"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."}}