{"id":"W2059365063","doi":"10.1007/s11069-011-9872-y","title":"Evaluating roadside rockmasses for rockfall hazards using LiDAR data: optimizing data collection and processing protocols","year":2011,"lang":"en","type":"article","venue":"Natural Hazards","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":83,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Rockfall; Lidar; Terrain; Hazard analysis; Point cloud; Landslide; Digital elevation model; Hazard; Ditch; Geology; Remote sensing; Environmental science; Computer science; Geotechnical engineering; Engineering; Cartography; Geography; Artificial intelligence","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":[0.001665754,0.0002747883,0.0003228013,0.00007908953,0.0009421456,0.0003447283,0.0008122065,0.0001466783,0.0001010019],"category_scores_gemma":[0.0005720733,0.0002127328,0.00003930692,0.0003353993,0.00007109781,0.00215607,0.0001882135,0.0003104909,0.00000583601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001859143,"about_ca_system_score_gemma":0.0003332736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001773317,"about_ca_topic_score_gemma":0.003026556,"domain_scores_codex":[0.9976662,0.0001670768,0.0003903747,0.0008354754,0.0004438194,0.0004970528],"domain_scores_gemma":[0.9986461,0.0001317371,0.0002169263,0.000648584,0.0002208111,0.0001358529],"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":[0.0005473276,0.00004459733,0.0826489,0.0003223959,0.00007628999,0.00001279205,0.001174914,0.0006834364,0.001500252,0.00000261209,0.0009422546,0.9120442],"study_design_scores_gemma":[0.0007045426,0.0002524453,0.03664866,0.0002680416,0.00007677297,0.00007925785,0.0004981775,0.9590079,0.0006802648,0.00009786752,0.001252383,0.0004337206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9736565,0.005482757,0.0061167,0.00009979041,0.0009308844,0.01073279,0.0006813326,0.0003902143,0.001908999],"genre_scores_gemma":[0.8596855,0.00001614903,0.1385825,0.00007496862,0.0002767245,0.0000721431,0.001000053,0.00001511094,0.00027679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9583244,"threshold_uncertainty_score":0.8674992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3165419703143929,"score_gpt":0.4012909978658282,"score_spread":0.08474902755143537,"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."}}