{"id":"W4206298316","doi":"10.1080/01490419.2022.2025503","title":"Case Study: Rigorous Boresight Alignment of a Marine Mobile LiDAR System Addressing the Specific Demands of Port Infrastructure Monitoring","year":2022,"lang":"en","type":"article","venue":"Marine Geodesy","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Mitacs","keywords":"Point cloud; Lidar; Computer science; Remote sensing; Mobile mapping; Orientation (vector space); Port (circuit theory); Georeference; Real-time computing; Geography; Engineering; Computer vision; Electronic engineering; Mathematics","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":[0.001157734,0.0005802977,0.0004047791,0.0003661558,0.000645342,0.0007145331,0.0008478505,0.001410074,0.0008902263],"category_scores_gemma":[0.003149042,0.0002210782,0.0003844463,0.0005600832,0.0006806114,0.0007517532,0.0008878363,0.0005512967,0.0003437407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004452214,"about_ca_system_score_gemma":0.0007987361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004301029,"about_ca_topic_score_gemma":0.004212436,"domain_scores_codex":[0.998642,0.0004497569,0.00008581043,0.0002183145,0.0004491204,0.0001548552],"domain_scores_gemma":[0.9977551,0.0007952979,0.0003148764,0.0005340177,0.0004424128,0.0001582362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001252909,0.0006216955,0.05070912,0.0004584526,0.0001210513,0.007964183,0.001183197,0.7462013,0.1016771,0.003707078,0.001921846,0.08418208],"study_design_scores_gemma":[0.0001650936,0.002417065,0.04049902,0.00004493273,0.0000929315,0.002855505,0.001387846,0.8416185,0.1032192,0.002330302,0.005257783,0.0001117976],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8532527,0.00008080729,0.1424131,0.0001631123,0.00004118158,0.0001770927,0.0002431228,0.000636174,0.002992717],"genre_scores_gemma":[0.9689461,0.00002015473,0.03042388,0.00002141619,0.000005776702,0.00003755344,0.0001235557,0.00002152375,0.0004001355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004301029,"threshold_uncertainty_score":0.008552015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01739279400550697,"score_gpt":0.2448970527924852,"score_spread":0.2275042587869782,"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."}}