{"id":"W1993352646","doi":"10.1016/s0924-2716(01)00030-2","title":"Digital image georeferencing from a multiple camera system by GPS/INS","year":2001,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Global Positioning System; Computer vision; Computer science; Differential GPS; Inertial navigation system; Artificial intelligence; Orientation (vector space); Trajectory; Calibration; Mobile mapping; Inertial measurement unit; Digital camera; Camera resectioning; Photogrammetry; Remote sensing; Geography; Mathematics; Point cloud","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023154,0.0006233093,0.0005026517,0.002215497,0.0004742673,0.0009367442,0.000469209,0.0004852754,0.006067518],"category_scores_gemma":[0.000706751,0.0004235299,0.0003425108,0.001983317,0.0002428684,0.0008792417,0.0007752488,0.000631007,0.003256166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005024737,"about_ca_system_score_gemma":0.0009484491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007576567,"about_ca_topic_score_gemma":0.01348412,"domain_scores_codex":[0.9995189,0.00004044973,0.00002113884,0.00009862558,0.0002772869,0.00004375353],"domain_scores_gemma":[0.9996492,0.00001828447,0.00003742211,0.00009208495,0.0001802114,0.00002281241],"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":[0.0004112653,0.0001031668,0.006959406,0.0002755258,0.00007797232,0.0002320847,0.0002605931,0.01851667,0.1856667,0.002597564,0.01438839,0.7705107],"study_design_scores_gemma":[0.0002691924,0.0005591194,0.1165069,0.000214673,0.0004427488,0.001495023,0.0006657012,0.3695378,0.3382632,0.006584682,0.1652038,0.0002571106],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.194644,0.0007612091,0.7501174,0.000509793,0.0007815983,0.0002167165,0.005040787,0.01168393,0.03624461],"genre_scores_gemma":[0.5795718,0.0004956769,0.398406,0.0001826412,0.0001462284,0.0001156151,0.004833476,0.000408738,0.01583979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007576567,"threshold_uncertainty_score":0.02029788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00661981641496788,"score_gpt":0.1926583827815384,"score_spread":0.1860385663665705,"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."}}