{"id":"W2003703294","doi":"10.5589/m10-054","title":"Estimation of biases in lidar system calibration parameters using overlapping strips","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Calibration; Ranging; Point cloud; Remote sensing; Terrain; Computer science; Position (finance); STRIPS; Elevation (ballistics); Orientation (vector space); Trajectory; Geography; Computer vision; Geodesy; Artificial intelligence; Mathematics; Statistics; Cartography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001346241,0.0005711695,0.0004260825,0.0007199895,0.0003117961,0.0006001876,0.0006458548,0.0004506117,0.0003248946],"category_scores_gemma":[0.005341477,0.0004411583,0.0004906986,0.0006801666,0.0003558716,0.001019284,0.0008328893,0.0004344866,0.0001550226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00042682,"about_ca_system_score_gemma":0.000848573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00242449,"about_ca_topic_score_gemma":0.003448435,"domain_scores_codex":[0.999055,0.0002647091,0.00005885363,0.000192813,0.0003648078,0.0000638341],"domain_scores_gemma":[0.9977726,0.0007667651,0.0004404121,0.0005673559,0.0004207623,0.00003203869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003524404,0.0001762295,0.05051328,0.0002649225,0.0002812791,0.0002896973,0.0005491185,0.5147089,0.1220083,0.004907916,0.0004119954,0.3055359],"study_design_scores_gemma":[0.00006545835,0.0001636551,0.03375028,0.00002802125,0.00006944109,0.0002697376,0.0001101289,0.9146239,0.04609944,0.003678738,0.001063489,0.00007779221],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4130925,0.0001265496,0.5856734,0.0000266547,0.00001457481,0.00004482845,0.00008462869,0.0003444318,0.0005924493],"genre_scores_gemma":[0.7851758,0.00009559191,0.214175,0.00001688244,0.000008601804,0.00004758654,0.0002116932,0.00003881478,0.0002299854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00242449,"threshold_uncertainty_score":0.007119715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02267978223821612,"score_gpt":0.2377047824900775,"score_spread":0.2150250002518614,"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."}}