{"id":"W1996585943","doi":"10.1016/j.rse.2013.04.019","title":"Automated reconstruction of tree and canopy structure for modeling the internal canopy radiation regime","year":2013,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Remote sensing; Canopy; Radiative transfer; Photosynthetically active radiation; Geometric modeling; Laser scanning; Environmental science; Computer science; Vegetation (pathology); Range (aeronautics); Mathematics; Optics; Laser; Geometry; Physics; Ecology; Geography","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":[0.0001312947,0.0001230839,0.0001656406,0.00003289675,0.0001147521,0.00001552885,0.00007859599,0.00007127061,0.00004142564],"category_scores_gemma":[0.00002388311,0.00009611224,0.00004965161,0.00006481043,0.0002288497,0.00007500716,0.00004881211,0.00008163576,0.000006122316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000130606,"about_ca_system_score_gemma":0.00000895002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003625617,"about_ca_topic_score_gemma":0.0001049994,"domain_scores_codex":[0.9990708,0.0000458295,0.000306399,0.000237556,0.0001819311,0.0001575304],"domain_scores_gemma":[0.9993662,0.00005701868,0.0002202998,0.0002948377,0.000009715029,0.00005194079],"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":[0.0000140094,0.000008182692,0.0002571364,0.0000118514,0.00002406771,1.356852e-7,0.0004325844,0.04028409,0.3565451,0.00001053399,0.0002325068,0.6021798],"study_design_scores_gemma":[0.000233872,0.00004244737,0.004856342,0.00003232562,0.00003274183,0.00003709098,0.000136479,0.9649695,0.02796073,0.001235946,0.0003635394,0.00009903656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688509,0.00004304809,0.02947399,0.0005198354,0.00006936082,0.0004474444,0.000007099901,0.00002891952,0.0005594761],"genre_scores_gemma":[0.9526482,0.00005352968,0.04711405,0.00003260531,0.00003093352,1.68461e-7,0.000007947527,0.0000152667,0.00009735534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9246854,"threshold_uncertainty_score":0.5480872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00800292559027017,"score_gpt":0.2096810084570082,"score_spread":0.2016780828667381,"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."}}