{"id":"W7099661168","doi":"","title":"CLUSTERING OF POINT PATTERNS DERIVED FROM LIDAR CANOPY HEIGHT DATA","year":2010,"lang":"en","type":"article","venue":"","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Smoothing; Lidar; Cluster analysis; Canopy; Point (geometry); Nonparametric statistics; Cluster (spacecraft); Closure (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003367465,0.0002559318,0.0002917712,0.001717533,0.0004144732,0.000744181,0.0004588368,0.0003830387,0.0003739681],"category_scores_gemma":[0.002311933,0.0002385381,0.000337959,0.001667859,0.0003614634,0.0003198756,0.0005224026,0.0002749827,0.0002315235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006472147,"about_ca_system_score_gemma":0.0008393803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03439634,"about_ca_topic_score_gemma":0.05862361,"domain_scores_codex":[0.9996797,0.00004035682,0.00001511866,0.00006786234,0.0001529295,0.00004405964],"domain_scores_gemma":[0.9993481,0.0002145741,0.00009170741,0.0001122952,0.0001986287,0.00003469744],"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":[0.000252651,0.0001373824,0.1695836,0.0002090545,0.0001214349,0.0009066921,0.001229548,0.4523654,0.08385105,0.00632978,0.001404892,0.2836086],"study_design_scores_gemma":[0.00001581257,0.0000448306,0.09393205,0.00002304759,0.00001665302,0.0004037022,0.0003463002,0.8870564,0.01206121,0.004463696,0.001585245,0.0000512236],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7525032,0.00009722431,0.2448882,0.00006308046,0.000005318333,0.00008973276,0.0006565906,0.0004000073,0.001296728],"genre_scores_gemma":[0.9291393,0.00005790012,0.06936911,0.000006444541,0.000003255403,0.000030877,0.001082403,0.00002234758,0.0002884636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03439634,"threshold_uncertainty_score":0.06839228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02525538929134582,"score_gpt":0.2349513246139747,"score_spread":0.2096959353226289,"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."}}