{"id":"W2097046261","doi":"10.1109/tgrs.2010.2099232","title":"Stability of Sample-Based Scanning-LiDAR-Derived Vegetation Metrics for Forest Monitoring","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of British Columbia","funders":"","keywords":"Lidar; Terrain; Remote sensing; Vegetation (pathology); Environmental science; Metric (unit); Calibration; Ranging; Standard deviation; Elevation (ballistics); Statistics; Mathematics; Geography; Geodesy; Cartography; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003657876,0.0001368902,0.0001545273,0.000120194,0.0004628175,0.00002660748,0.00009721896,0.00007121149,0.0000077699],"category_scores_gemma":[0.00004529644,0.0001281782,0.00008175178,0.0006014481,0.0004304571,0.000146531,0.000002441578,0.0001127473,0.000003873802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007814521,"about_ca_system_score_gemma":0.00003017608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002612475,"about_ca_topic_score_gemma":0.0002700602,"domain_scores_codex":[0.9988168,0.00003841921,0.0002526399,0.0003833513,0.0002543967,0.0002543822],"domain_scores_gemma":[0.9991962,0.0002565696,0.0001170409,0.0002757995,0.00004969541,0.0001047223],"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":[0.0000631865,0.0000922782,0.000668635,0.00003994588,0.000008920697,7.397688e-7,0.001901922,0.003370395,0.09587824,0.00000547688,0.000001330934,0.8979689],"study_design_scores_gemma":[0.0003890521,0.00020504,0.0281149,0.00008566451,0.00005259063,0.000006709534,0.0004497467,0.4011831,0.5685173,0.0007026889,0.00005636355,0.0002367876],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4537517,0.000004741333,0.5456877,0.00002631404,0.0001373842,0.0001710564,0.00000436264,0.00002639671,0.0001902983],"genre_scores_gemma":[0.7156702,0.000007549771,0.2842712,0.00001978209,0.00001068725,2.322563e-7,6.63373e-7,0.000009255737,0.00001051245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8977321,"threshold_uncertainty_score":0.5226957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04415924963861854,"score_gpt":0.2590301777051225,"score_spread":0.2148709280665039,"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."}}