{"id":"W80323238","doi":"10.1007/978-3-642-59742-8_54","title":"A GPS/INS/imaging system for kinematic mapping in fully digital mode","year":2000,"lang":"en","type":"book-chapter","venue":"International Association of Geodesy symposia","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; Inertial navigation system; Trajectory; Computer vision; Kinematics; Computer science; Inertial measurement unit; Orientation (vector space); Artificial intelligence; Remote sensing; Geography","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":[0.0001855894,0.0006095893,0.0004442885,0.0005895829,0.0003811175,0.0006361557,0.0008125892,0.0006921554,0.02858675],"category_scores_gemma":[0.0002686982,0.0005126228,0.0002173976,0.0006772652,0.0002282415,0.0008740828,0.0006435605,0.0005203972,0.01642847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001970905,"about_ca_system_score_gemma":0.0005356797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001607978,"about_ca_topic_score_gemma":0.002087148,"domain_scores_codex":[0.9998392,0.00001386121,0.000009523363,0.00003081403,0.00009350175,0.00001307655],"domain_scores_gemma":[0.9998577,0.0000223758,0.000004600318,0.00003923744,0.00006449017,0.00001153325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002266647,0.00008515512,0.0006864364,0.0003685466,0.00003547455,0.0002285113,0.0002309103,0.005403914,0.1209558,0.01361356,0.1053774,0.7527876],"study_design_scores_gemma":[0.0001230038,0.0004910271,0.004142674,0.0001558692,0.000134838,0.002007264,0.00009349206,0.0801255,0.1227407,0.00874067,0.7811243,0.0001206966],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01019617,0.001230464,0.863281,0.000351707,0.0006636758,0.0003201196,0.002479005,0.03100476,0.09047311],"genre_scores_gemma":[0.1173183,0.001726658,0.6697847,0.0008002786,0.0003069452,0.0004412302,0.007239239,0.002216504,0.2001662],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02858675,"threshold_uncertainty_score":0.0956322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005590871316852202,"score_gpt":0.1939070565855888,"score_spread":0.1883161852687366,"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."}}