{"id":"W2989849941","doi":"10.3390/ijgi8120525","title":"Automated Method for Detection of Missing Road Point Regions in Mobile Laser Scanning Data","year":2019,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Pixel; Computer science; Artificial intelligence; Computer vision; MATLAB; Laser scanning; Point (geometry); Geodetic datum; Point cloud; Image (mathematics); Path (computing); Plane (geometry); Missing data; Image plane; Laser; Mathematics; Optics; Geography; Geodesy; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008479207,0.00007343839,0.0001339685,0.0002156628,0.00004390564,0.00005700828,0.0003887625,0.00005419437,0.00005672728],"category_scores_gemma":[0.0001576649,0.0000682855,0.00005992651,0.0001651483,0.00002578017,0.002158989,0.00009198557,0.000116351,0.0000487285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002027317,"about_ca_system_score_gemma":0.0000341806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003254278,"about_ca_topic_score_gemma":0.00002797744,"domain_scores_codex":[0.998789,0.00003737351,0.0006165609,0.00007976156,0.000374488,0.0001027988],"domain_scores_gemma":[0.9988617,0.00008696117,0.0006642048,0.0002036358,0.0001440207,0.00003950293],"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":[0.0002412145,0.00009047572,0.001364393,0.00002654702,0.00007449817,0.000001602649,0.002221298,0.109633,0.0415038,0.00008475989,0.002903879,0.8418545],"study_design_scores_gemma":[0.001182612,0.0001138277,0.0183011,0.000145621,0.0000231128,0.0001610893,0.0008672078,0.917483,0.01913857,0.0006276668,0.04182085,0.0001353706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4212312,0.00001807319,0.5741665,0.0009502908,0.0007270034,0.0004265165,0.00005268658,0.0000372366,0.002390492],"genre_scores_gemma":[0.9541692,0.00001059645,0.04552514,0.0001389118,0.00004246339,0.000002073862,0.00007360036,0.000005198791,0.00003279894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8417192,"threshold_uncertainty_score":0.2784602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282010685046598,"score_gpt":0.3074465283982945,"score_spread":0.2946264215478285,"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."}}