{"id":"W1540266240","doi":"","title":"Accelerated robust point cloud registration in natural environments through positive and unlabeled learning","year":2013,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Point cloud; Pairwise comparison; Computer vision; Image registration; Robot; Machine learning; Mobile robot; Pattern recognition (psychology); 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":[0.001607425,0.0007565543,0.001245228,0.0009620805,0.0008437338,0.0007825606,0.00187574,0.001167791,0.001019639],"category_scores_gemma":[0.006080992,0.0005516373,0.0007220564,0.001024197,0.001394902,0.002276909,0.002367963,0.001256096,0.0007385679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005665227,"about_ca_system_score_gemma":0.001131946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005144329,"about_ca_topic_score_gemma":0.007607658,"domain_scores_codex":[0.9987249,0.0003209963,0.00004853073,0.000371793,0.0004083762,0.0001253848],"domain_scores_gemma":[0.9968451,0.001121343,0.0003748066,0.001008089,0.0005637494,0.0000869127],"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":[0.00056077,0.0003257584,0.003977492,0.00009750643,0.00006587801,0.000177883,0.0001567906,0.4716508,0.04298292,0.005983512,0.001688391,0.4723322],"study_design_scores_gemma":[0.00001052005,0.00005720924,0.0006206747,0.000003649077,0.000005949633,0.00007388656,0.00001944799,0.9887116,0.007687399,0.002418159,0.0003816698,0.000009809456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06109751,0.0001065063,0.9367132,0.00008410273,0.00002526959,0.00005250066,0.00004469709,0.00131944,0.0005569429],"genre_scores_gemma":[0.6186655,0.0001093442,0.3787183,0.00008026721,0.00005144161,0.0001042206,0.0004610387,0.0002273141,0.001582507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005144329,"threshold_uncertainty_score":0.01022875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06487840364842372,"score_gpt":0.2634298915574278,"score_spread":0.1985514879090041,"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."}}