{"id":"W2104863115","doi":"10.1080/2150704x.2013.846487","title":"A marked point process for automated building detection from lidar point-clouds","year":2013,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"York University","keywords":"Reversible-jump Markov chain Monte Carlo; Lidar; Point cloud; Ranging; Computer science; Maximum a posteriori estimation; Markov chain Monte Carlo; Posterior probability; Algorithm; Process (computing); Point (geometry); Bayesian probability; Point process; Artificial intelligence; Remote sensing; Mathematics; Maximum likelihood; Geography; Statistics; Geometry","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":[0.0007934278,0.0004198762,0.0005183097,0.0006962917,0.0004516711,0.0005545045,0.001292564,0.0007432887,0.00156715],"category_scores_gemma":[0.002604052,0.0004098731,0.0005389189,0.0006653303,0.0007997524,0.0008371266,0.0009908066,0.001036159,0.0006721914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005534036,"about_ca_system_score_gemma":0.0009064552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001766537,"about_ca_topic_score_gemma":0.002100569,"domain_scores_codex":[0.9993591,0.0001464711,0.00002393366,0.0001397475,0.0002939313,0.00003683518],"domain_scores_gemma":[0.9991623,0.0004564529,0.00008901465,0.00009506896,0.0001609641,0.00003624555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001635557,0.000109593,0.001535751,0.0001212033,0.00006382971,0.0002261554,0.000122831,0.6504223,0.02196396,0.06712098,0.001468911,0.2566809],"study_design_scores_gemma":[0.000005540568,0.00001590423,0.000131569,0.000003495612,0.000003111025,0.00003252819,0.000003223283,0.9924839,0.001722609,0.004901217,0.0006899071,0.000007064254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002611994,0.00002640998,0.9969754,0.00001602361,0.000009706994,0.00001408913,0.00001327866,0.000163803,0.0001691767],"genre_scores_gemma":[0.2157084,0.0001082085,0.7827087,0.00004738782,0.00003934156,0.0001537122,0.0001670372,0.00005982583,0.001007309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001766537,"threshold_uncertainty_score":0.005242646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008926932244175245,"score_gpt":0.2360576531787115,"score_spread":0.2271307209345363,"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."}}