{"id":"W2784901649","doi":"","title":"General approach for the mounting parameters calibration of photogrammetric mobile mapping systems","year":2011,"lang":"en","type":"article","venue":"32nd Asian Conference on Remote Sensing 2011, ACRS 2011","topic":"Satellite Image Processing and Photogrammetry","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Photogrammetry; Calibration; Mobile mapping; Computer science; Remote sensing; Computer vision; Artificial intelligence; Computer graphics (images); Geography; Mathematics; Point cloud","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":[0.0006380295,0.001400826,0.000697123,0.001246198,0.0007662096,0.001250735,0.002084224,0.001467643,0.008150437],"category_scores_gemma":[0.0009218419,0.0007439315,0.001146069,0.001285945,0.0004536462,0.001015787,0.001327545,0.001558474,0.008207632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005833925,"about_ca_system_score_gemma":0.0008073513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003321957,"about_ca_topic_score_gemma":0.00506496,"domain_scores_codex":[0.9991459,0.0001342468,0.00004392723,0.000209245,0.0004249352,0.0000417477],"domain_scores_gemma":[0.9998146,0.00002413614,0.00001569197,0.00004848306,0.00009127676,0.000005835156],"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":[0.00007334539,0.00007088725,0.000923221,0.0009185962,0.0001387021,0.0004739413,0.0003641591,0.1696163,0.109536,0.0855935,0.01039374,0.6218976],"study_design_scores_gemma":[0.00004146107,0.0001575025,0.003202574,0.0001920108,0.0001130491,0.001345999,0.000154905,0.7300808,0.0674802,0.05104038,0.146059,0.0001320092],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004666288,0.0001711302,0.9977114,0.00002076873,0.00003497399,0.00003482867,0.00004581208,0.0002914876,0.001222929],"genre_scores_gemma":[0.05114946,0.001290631,0.9321413,0.00008370692,0.0001053727,0.0003654375,0.0005683062,0.0002751089,0.01402071],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008150437,"threshold_uncertainty_score":0.02726591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05664167670809923,"score_gpt":0.2365731374906466,"score_spread":0.1799314607825473,"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."}}