{"id":"W2138861063","doi":"10.5589/m08-043","title":"A linear regression method for tree canopy height estimation using airborne lidar data","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lidar; Canopy; Mean squared error; Tree canopy; Remote sensing; Ranging; Tree (set theory); Forest inventory; Environmental science; Crown (dentistry); Linear regression; Regression analysis; Mathematics; Geography; Statistics; Forest management; Agroforestry; Geodesy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002129594,0.0009502441,0.000593628,0.001279897,0.0003411662,0.000446702,0.0009676044,0.0006049121,0.001204861],"category_scores_gemma":[0.006152768,0.0005294532,0.0008037703,0.001355218,0.0002465303,0.001171074,0.000513968,0.001061915,0.001272118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003906707,"about_ca_system_score_gemma":0.0006563294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003521289,"about_ca_topic_score_gemma":0.003872861,"domain_scores_codex":[0.9981822,0.0005311287,0.0001012898,0.0004255175,0.0006877823,0.00007212724],"domain_scores_gemma":[0.997043,0.001548017,0.0002951186,0.0002305582,0.0008522284,0.00003109952],"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":[0.0001925564,0.0002096528,0.007947004,0.0004131985,0.0003866405,0.0001976439,0.0002003184,0.1309298,0.07157958,0.003271225,0.002734838,0.7819375],"study_design_scores_gemma":[0.00004159836,0.0001762984,0.005403849,0.00002830889,0.00009732411,0.0002894164,0.00004368785,0.9593422,0.02925279,0.0009984635,0.004238857,0.00008715612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007334072,0.0001240526,0.9913731,0.00002485104,0.00001748366,0.00002851869,0.00006505892,0.0008124255,0.0002202477],"genre_scores_gemma":[0.08502036,0.0002479913,0.9126582,0.0000500986,0.00004196787,0.0001637883,0.0002522692,0.0001834571,0.001381778],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003521289,"threshold_uncertainty_score":0.01126254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05593861350532196,"score_gpt":0.3085742981839071,"score_spread":0.2526356846785851,"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."}}