{"id":"W291221375","doi":"","title":"Spatial quantification of vegetation density from terrestrial laser scanner data for characterization of 3D forest structure at plot level","year":2008,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Vegetation (pathology); Point cloud; Lidar; Laser scanning; Remote sensing; Voxel; Leaf area index; Environmental science; Plot (graphics); Forest structure; Focus (optics); Volume (thermodynamics); Geography; Computer science; Mathematics; Laser; Ecology; Statistics; Artificial intelligence; Canopy; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00114482,0.0002627787,0.0003899104,0.00008582824,0.0002702639,0.00006094921,0.001069561,0.0003116472,0.00006638723],"category_scores_gemma":[0.0007205248,0.0002797703,0.0001118957,0.0002098078,0.0003879334,0.0001729145,0.001238062,0.0002359725,0.00001407629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000145648,"about_ca_system_score_gemma":0.0001165161,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01057018,"about_ca_topic_score_gemma":0.01879526,"domain_scores_codex":[0.9966701,0.001078194,0.0007206243,0.0008599127,0.0004696712,0.0002014896],"domain_scores_gemma":[0.9949604,0.0005659884,0.001227891,0.002758416,0.0003972751,0.0000900669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002161021,0.0007560229,0.0267884,0.0002073468,0.0001519177,7.722726e-7,0.006402851,0.004307607,0.8704338,0.0005317922,0.001058448,0.08914495],"study_design_scores_gemma":[0.0008776532,0.000001231903,0.3557831,0.0005122107,0.0001392805,0.000003179212,0.00001913119,0.2144751,0.4235677,0.001157659,0.003057808,0.0004059385],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7148924,0.00001875252,0.2814053,0.0005839157,0.0001885687,0.0006625333,0.001724824,0.00004105704,0.0004826553],"genre_scores_gemma":[0.9333808,0.0001032164,0.04172128,0.00001600662,0.00005224267,0.00001315634,0.02420355,0.00003825077,0.0004714362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4468661,"threshold_uncertainty_score":0.9999654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03565631970896949,"score_gpt":0.2404032563994664,"score_spread":0.2047469366904969,"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."}}