{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002735148,0.00131707,0.0008399872,0.001839772,0.0008467658,0.001928282,0.001897673,0.001856204,0.002041927],"category_scores_gemma":[0.008967206,0.0006104117,0.001218739,0.001023972,0.001697391,0.003036109,0.001757902,0.002556178,0.0006660021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001067402,"about_ca_system_score_gemma":0.001115976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00624052,"about_ca_topic_score_gemma":0.005452801,"domain_scores_codex":[0.9993,0.0002287669,0.00004800055,0.0001240195,0.0002142133,0.00008505514],"domain_scores_gemma":[0.9962285,0.002099891,0.0006164944,0.0002237464,0.0007194149,0.0001119897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001259781,0.00007112774,0.001225038,0.0001834315,0.0000526063,0.0003328885,0.0002641547,0.4693586,0.009496744,0.5053313,0.00245274,0.01121878],"study_design_scores_gemma":[0.000002020771,0.0000263008,0.0004875957,0.00001957614,0.0000142629,0.000138824,0.00003729459,0.9501303,0.0004380141,0.04720349,0.001474467,0.00002783623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009524088,0.0007494747,0.9822209,0.000455047,0.0001058943,0.00002982847,0.00009985223,0.00006257204,0.006752321],"genre_scores_gemma":[0.6612262,0.005041497,0.3083496,0.000814959,0.0007131972,0.0004435015,0.000419175,0.0004405536,0.02255139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00624052,"threshold_uncertainty_score":0.01446503,"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."}}