{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005596269,0.00008437427,0.00007360295,0.00005755409,0.00006216563,0.00005183015,0.00009818053,0.00006010191,0.00001343398],"category_scores_gemma":[0.00001926002,0.00008948048,0.000007820887,0.00009310044,0.00001248913,0.0003536505,0.00005680039,0.00004433282,0.000001355166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000629021,"about_ca_system_score_gemma":0.00001132585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004593755,"about_ca_topic_score_gemma":0.00001181879,"domain_scores_codex":[0.9995207,0.000005703401,0.0001218351,0.0001523639,0.00008736732,0.0001119987],"domain_scores_gemma":[0.9995763,0.000008389473,0.00001692494,0.0003223415,0.0000357751,0.00004022307],"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":[0.000001716903,0.000005343803,0.00001050005,0.00001531651,0.000006606641,0.00000103644,0.00006309252,0.9971305,0.0007599206,0.001714522,0.00006362161,0.0002278794],"study_design_scores_gemma":[0.0002923848,0.000006551954,0.00001154165,0.00002978713,0.00001756658,0.000003679375,0.00003025913,0.9977539,0.0004077116,0.001327391,0.000005311623,0.000113981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01675874,0.00008569173,0.9820103,0.00005579105,0.0000680279,0.00008344126,0.00001866496,0.0001302753,0.0007890419],"genre_scores_gemma":[0.4348067,0.0001017869,0.5646278,0.00008173496,0.00004920774,7.730177e-7,0.0002917036,0.00002787815,0.00001240999],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.418048,"threshold_uncertainty_score":0.3648908,"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."}}