{"id":"W4361007733","doi":"10.3390/s23073491","title":"Real-Time Safe Landing Zone Identification Based on Airborne LiDAR","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Point cloud; Computer science; Identification (biology); Real-time computing; Software; Landing gear; Data processing; Simulation; Remote sensing; Aerospace engineering; Engineering; Artificial intelligence; Database","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":[0.000224642,0.0005316733,0.0004198526,0.001100408,0.0003088367,0.0005911466,0.0005589129,0.000411811,0.001003988],"category_scores_gemma":[0.0005254066,0.0002690553,0.0003197512,0.000437778,0.0001435088,0.0006164153,0.0005857082,0.0002782113,0.0005553984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002128526,"about_ca_system_score_gemma":0.000576201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003218744,"about_ca_topic_score_gemma":0.004612064,"domain_scores_codex":[0.9997891,0.00002517575,0.000009608032,0.00003985903,0.00009429338,0.00004194663],"domain_scores_gemma":[0.999795,0.00004145337,0.00002654721,0.0000191542,0.00009945492,0.00001837736],"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":[0.0005037478,0.0002340615,0.0157609,0.0002550613,0.00007171739,0.0007847567,0.0005316813,0.2093694,0.1905074,0.001931291,0.003920861,0.5761292],"study_design_scores_gemma":[0.00001938519,0.0000722124,0.002661275,0.00001513024,0.0000127136,0.0001261704,0.0001797882,0.9794512,0.01565981,0.0006687089,0.001113251,0.00002031271],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2238907,0.0002669774,0.7699257,0.0001260069,0.00007911185,0.0001188735,0.0002214838,0.002484946,0.002886257],"genre_scores_gemma":[0.7573444,0.0001103583,0.2411231,0.00004216799,0.00001593369,0.00006714192,0.0002892119,0.00004928941,0.0009584791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003218744,"threshold_uncertainty_score":0.006399989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0113588760248503,"score_gpt":0.2409291015905327,"score_spread":0.2295702255656824,"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."}}