{"id":"W1854621565","doi":"10.1007/11866763_74","title":"A High-Order Solution for the Distribution of Target Registration Error in Rigid-Body Point-Based Registration","year":2006,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Point (geometry); Image registration; Distribution (mathematics); Point distribution model; Computer vision; Rigid body; Artificial intelligence; Algorithm; Mathematics; Geometry; Mathematical analysis","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":[0.0004804899,0.0001105178,0.000115305,0.0001116755,0.0000935545,0.00006535997,0.0001736337,0.00007457478,0.000001484094],"category_scores_gemma":[0.0001118184,0.00009197342,0.00002923712,0.0008818142,0.0001348887,0.0001799888,0.000009781566,0.00009614997,3.971927e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001609026,"about_ca_system_score_gemma":0.00008120463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003759529,"about_ca_topic_score_gemma":0.0006511519,"domain_scores_codex":[0.9989921,0.00002458914,0.0003194389,0.000218754,0.0002312376,0.0002138901],"domain_scores_gemma":[0.9993585,0.0001827788,0.00008210001,0.0002235521,0.0001359382,0.00001709671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008800748,0.00002709999,0.0003917935,0.00002700126,9.589935e-7,5.10309e-7,0.00003049885,0.9870133,0.007572311,0.001431056,0.00004222025,0.00345444],"study_design_scores_gemma":[0.0003262751,0.00005257184,0.00693044,0.00003537702,0.000003467251,9.42286e-7,4.860559e-7,0.9556703,0.03216001,0.004692172,0.00002929625,0.00009868435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0293328,0.00005053225,0.969016,0.0008487917,0.0003617337,0.0003312037,0.0000107171,0.00004356351,0.000004670971],"genre_scores_gemma":[0.9085528,0.000001646089,0.09115372,0.00004573342,0.00009487303,0.00001426199,0.0001294018,0.0000070935,4.887307e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.87922,"threshold_uncertainty_score":0.3750567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008881138588796175,"score_gpt":0.2245808183224281,"score_spread":0.2156996797336319,"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."}}