{"id":"W2098655291","doi":"10.2514/6.2006-6709","title":"Feature Matching Navigation Techniques for Lidar-Based Planetary Exploration","year":2006,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control Conference and Exhibit","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"NGC Aerospace (Canada)","funders":"Canadian Space Agency","keywords":"Lidar; Computer science; Matching (statistics); Feature (linguistics); Remote sensing; Artificial intelligence; Computer vision; Geology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001666262,0.0002123929,0.0002319917,0.00008316345,0.0002190682,0.0001947654,0.00005812037,0.0001692757,0.00000270833],"category_scores_gemma":[0.000008021199,0.0002122673,0.00003770084,0.0001203102,0.00005698442,0.0003585869,0.000004683589,0.0001152216,0.000001577143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002598996,"about_ca_system_score_gemma":0.00003327072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006214107,"about_ca_topic_score_gemma":0.00003183189,"domain_scores_codex":[0.9991083,0.00002831358,0.0002785733,0.00024976,0.0001282617,0.0002067922],"domain_scores_gemma":[0.9994673,0.00008004441,0.0000761817,0.00013275,0.0001936293,0.00005007378],"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":[0.0008124672,0.0002241452,0.02938711,0.003506256,0.0003054451,0.0000321571,0.001602264,0.3295538,0.2774927,0.2019381,0.01694446,0.1382011],"study_design_scores_gemma":[0.003229507,0.0002194622,0.005704094,0.0008586406,0.0001586276,0.00001234661,0.0002310898,0.8911725,0.03063249,0.04865609,0.01830738,0.0008177831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1479886,0.001166374,0.8484252,0.0007767168,0.0001332047,0.000637194,0.0000602357,0.0002989312,0.0005135612],"genre_scores_gemma":[0.9950435,0.00009119102,0.003186864,0.0001950897,0.0001945107,0.0001135952,0.001071091,0.00002732856,0.00007684589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8470549,"threshold_uncertainty_score":0.8656009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008350878168793813,"score_gpt":0.2077453120643428,"score_spread":0.199394433895549,"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."}}