{"id":"W2789492941","doi":"10.1016/j.rse.2017.12.006","title":"A mathematical framework to describe the effect of beam incidence angle on metrics derived from airborne LiDAR: The case of forest canopies approaching turbid medium behaviour","year":2018,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Remote sensing; Canopy; Environmental science; Nadir; Point cloud; Tree canopy; Point (geometry); Mathematics; Geology; Computer science; Geography; Geometry; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008038232,0.0002335553,0.0003747221,0.00005834472,0.0002433293,0.00001750951,0.0003275075,0.0001157864,0.0000471474],"category_scores_gemma":[0.0004907799,0.0001419359,0.0001272907,0.0003334048,0.001133524,0.00003658096,0.0003116787,0.0002693182,0.00005226262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001564097,"about_ca_system_score_gemma":0.0000115443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00493712,"about_ca_topic_score_gemma":0.0003097237,"domain_scores_codex":[0.9981138,0.0002304189,0.0004704689,0.0003753584,0.0005365039,0.000273513],"domain_scores_gemma":[0.9972377,0.001278882,0.0003026616,0.001060148,0.0000121127,0.0001085239],"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":[0.0005704316,0.0006598859,0.01229887,0.0001634669,0.0003852473,0.0002124585,0.03931171,0.03908606,0.4242343,0.0002593661,0.000550273,0.4822679],"study_design_scores_gemma":[0.0005237368,0.001749,0.09738842,0.0006158529,0.0004996907,0.0002889043,0.002141754,0.03222789,0.860056,0.003727978,0.0002646124,0.0005161797],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.931293,0.00003408723,0.06709129,0.0004671695,0.00006252161,0.0005741781,0.00001320863,0.0000145251,0.0004500458],"genre_scores_gemma":[0.9428195,0.000008849987,0.05697718,0.0000778401,0.00006562204,4.663897e-7,0.000003459582,0.00002699241,0.00002010417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4817517,"threshold_uncertainty_score":0.7463481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346051556718258,"score_gpt":0.2436801266989484,"score_spread":0.2302196111317658,"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."}}