{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006340967,0.0001582552,0.000254069,0.0001610213,0.0006092514,0.00003436764,0.0002958397,0.0001006763,0.00002387291],"category_scores_gemma":[0.0003293846,0.0001412938,0.00008620424,0.0003247569,0.000148991,0.0002743643,0.00003901255,0.0002237831,0.00001366721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004089309,"about_ca_system_score_gemma":0.0005391609,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02258642,"about_ca_topic_score_gemma":0.02007405,"domain_scores_codex":[0.9986352,0.00009117051,0.0004157367,0.0002734451,0.000237175,0.0003472643],"domain_scores_gemma":[0.9984053,0.00009777597,0.0003534454,0.0005461921,0.00007768732,0.0005195402],"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":[0.00002037864,0.000005229613,0.00009378856,0.000007451071,0.00002220666,0.0001561684,0.0007039799,0.005149272,0.009768199,0.00000370895,0.003638592,0.980431],"study_design_scores_gemma":[0.0003301864,0.0000424547,0.0007885993,0.0001471251,0.00006153141,0.002862925,0.00007818748,0.921462,0.004462514,0.0003448236,0.06923596,0.0001837035],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1707924,0.0001062918,0.8267788,0.00105056,0.000251464,0.0001645445,0.000017178,0.00001128524,0.0008274678],"genre_scores_gemma":[0.1716125,0.00001116837,0.8278015,0.0001699997,0.0002223879,5.752602e-9,0.00001866959,0.00002782775,0.0001360188],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9802473,"threshold_uncertainty_score":0.997807,"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."}}