{"id":"W2040383628","doi":"10.1016/j.rse.2007.04.001","title":"Development of a simulation model to predict LiDAR interception in forested environments","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Commonwealth Scientific and Industrial Research Organisation","keywords":"Lidar; Interception; Remote sensing; Environmental science; Range (aeronautics); Canopy; Laser scanning; Ranging; Tree canopy; Computer science; Geography; Laser; Ecology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004930861,0.000682231,0.0007827336,0.0006289749,0.000853608,0.0008344026,0.001141765,0.001558893,0.002783912],"category_scores_gemma":[0.001955257,0.0006899639,0.0007899755,0.0005536516,0.0004567385,0.0008664309,0.0005240298,0.0008164358,0.0003535527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001227492,"about_ca_system_score_gemma":0.001966481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05509485,"about_ca_topic_score_gemma":0.02548918,"domain_scores_codex":[0.9998767,0.00003713262,0.000009320955,0.00002346503,0.00002850444,0.00002491996],"domain_scores_gemma":[0.9989992,0.0006195166,0.00007465378,0.000042553,0.0001875776,0.00007648962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007230489,0.00001171752,0.0004540031,0.000003474518,0.000004634495,0.00001383057,0.000004032622,0.9985936,0.00009385499,0.0002688812,0.00005341208,0.0004913923],"study_design_scores_gemma":[0.000003340378,0.000002902419,0.00003939598,6.736679e-7,0.000001591768,0.000001425326,0.000001887958,0.9997529,0.00006663499,0.00008138411,0.00004664093,0.000001200283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6300869,0.0003022411,0.3481661,0.0007452228,0.0001682345,0.0002320946,0.001385968,0.001811356,0.01710185],"genre_scores_gemma":[0.9587867,0.0001511332,0.03723515,0.00005641847,0.00001768041,0.0001867449,0.0005773924,0.00008986374,0.002898854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05509485,"threshold_uncertainty_score":0.1095483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02218383132527471,"score_gpt":0.2620928705773586,"score_spread":0.2399090392520839,"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."}}