{"id":"W4246576348","doi":"10.5194/isprsarchives-xli-b1-985-2016","title":"CO-REGISTRATION OF DSMs GENERATED BY UAV AND TERRESTRIAL LASER SCANNING SYSTEMS","year":2016,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer vision; Lidar; Artificial intelligence; Feature (linguistics); Overlay; Remote sensing; Geography","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":[0.0003943419,0.0005214887,0.0005190626,0.002642623,0.0002519272,0.001044637,0.0005689448,0.0004786776,0.001005475],"category_scores_gemma":[0.001351889,0.0003696214,0.0007408024,0.003406239,0.0002665427,0.0008170751,0.001476974,0.0005248387,0.001022427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003330104,"about_ca_system_score_gemma":0.0006902194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002771025,"about_ca_topic_score_gemma":0.004017915,"domain_scores_codex":[0.9990588,0.0001376576,0.00006584685,0.0001696245,0.0004462239,0.0001218254],"domain_scores_gemma":[0.9994491,0.00004059499,0.00006536007,0.0001799962,0.0002404083,0.0000244997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005378604,0.0002189589,0.01591001,0.0003613197,0.0002731839,0.0007922134,0.0004852662,0.1630881,0.1333094,0.006324072,0.007405324,0.6712945],"study_design_scores_gemma":[0.00003401892,0.0001950569,0.02460209,0.00005093722,0.00008315871,0.0005871631,0.0006288567,0.8946825,0.05544687,0.004390593,0.01920183,0.00009699448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1549024,0.0005358629,0.8346977,0.0001593907,0.0002755378,0.0001618798,0.001592646,0.002565283,0.005109269],"genre_scores_gemma":[0.6635653,0.0003509121,0.329194,0.00005028763,0.00005454513,0.0001026372,0.004346381,0.0001696158,0.002166285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002771025,"threshold_uncertainty_score":0.005509794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01494751351879952,"score_gpt":0.2502088609437975,"score_spread":0.2352613474249979,"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."}}