{"id":"W2148502089","doi":"10.1109/im.2003.1240257","title":"Recursive model optimization using ICP and free moving 3D data acquisition","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Computer vision; Position (finance); Artificial intelligence; Object (grammar); Tracking (education); Range (aeronautics); Image resolution; Resolution (logic); Object detection; Algorithm; Pattern recognition (psychology); Engineering","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":[0.0007661456,0.00116008,0.001407488,0.001344995,0.0005319529,0.001470413,0.002016803,0.001461837,0.001531656],"category_scores_gemma":[0.002551392,0.001460276,0.001896663,0.001564425,0.0009557578,0.001795549,0.002305029,0.001701012,0.00109424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008898366,"about_ca_system_score_gemma":0.001525194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007969814,"about_ca_topic_score_gemma":0.007616526,"domain_scores_codex":[0.9987929,0.0002285076,0.00004409644,0.0002573046,0.0005856815,0.00009149621],"domain_scores_gemma":[0.9993302,0.0002324389,0.00009285581,0.0001820728,0.0001372257,0.00002527377],"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":[0.0000506149,0.00003724823,0.0003895431,0.0001206258,0.0001069062,0.000139601,0.0002011244,0.7366372,0.01508264,0.02434164,0.002737198,0.2201558],"study_design_scores_gemma":[0.000006230958,0.00001474777,0.00009346523,0.000005040626,0.000008492735,0.00006362425,0.00001150553,0.988422,0.00359821,0.005342932,0.002418123,0.00001555738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008918616,0.00004296898,0.9982902,0.00002319814,0.000005675446,0.000008346177,0.00001213719,0.0004428069,0.0002827915],"genre_scores_gemma":[0.0493663,0.000118881,0.9490058,0.00003881874,0.00001765928,0.0001022991,0.0001602942,0.0002869082,0.0009030483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007969814,"threshold_uncertainty_score":0.01584685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03166563757885937,"score_gpt":0.2353519445183906,"score_spread":0.2036863069395312,"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."}}