{"id":"W4252405433","doi":"10.1145/2010324.1964947","title":"GlobFit","year":2011,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":166,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Seventh Framework Programme; National Natural Science Foundation of China","keywords":"RANSAC; Robustness (evolution); Outlier; Computer science; Ground truth; Set (abstract data type); Algorithm; Geometric primitive; Object (grammar); Noise (video); Global optimization; Artificial intelligence; Image (mathematics)","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":[0.001259058,0.002254364,0.001725206,0.00235027,0.001070742,0.003727928,0.004834571,0.00284497,0.09388204],"category_scores_gemma":[0.004221545,0.001257069,0.002747958,0.002462523,0.0008075917,0.002987812,0.005084664,0.002426849,0.06517132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009179736,"about_ca_system_score_gemma":0.001412537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004482455,"about_ca_topic_score_gemma":0.007735949,"domain_scores_codex":[0.9989189,0.0001574976,0.00005528624,0.00032869,0.0004176105,0.0001220628],"domain_scores_gemma":[0.9991724,0.000165251,0.00004384239,0.0003760463,0.0001910949,0.00005139846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004907045,0.0001778884,0.002937597,0.001160011,0.0004725567,0.0004853435,0.0004277779,0.08350864,0.006843569,0.04953001,0.4551558,0.3988101],"study_design_scores_gemma":[0.0001858384,0.0001010504,0.001223103,0.000188731,0.00006656927,0.0006308118,0.0002576058,0.4481889,0.009947238,0.0720919,0.466989,0.0001292192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004194953,0.0006486742,0.7287468,0.0006121783,0.0005187946,0.0002074167,0.01608061,0.225697,0.02329361],"genre_scores_gemma":[0.07572224,0.001225274,0.700135,0.0009830907,0.0002349522,0.0009638756,0.1075107,0.07068287,0.042542],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09388204,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03453351065816071,"score_gpt":0.205509405961013,"score_spread":0.1709758953028523,"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."}}