{"id":"W2114191733","doi":"10.1139/x03-225","title":"Assessing forest metrics with a ground-based scanning lidar","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":422,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lidar; Point cloud; Remote sensing; Diameter at breast height; Deciduous; Environmental science; Forest inventory; Canopy; Tree canopy; Sampling (signal processing); Laser scanning; Geography; Forestry; Forest management; Ecology; Computer science; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001505539,0.000434582,0.0003694579,0.00122177,0.0002205992,0.0004695007,0.0003833621,0.0003182206,0.0006094728],"category_scores_gemma":[0.003012713,0.0001401152,0.000189967,0.0009015661,0.0001443884,0.0008824857,0.0004138365,0.0001262395,0.0001530645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003619484,"about_ca_system_score_gemma":0.0002752377,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002432301,"about_ca_topic_score_gemma":0.005054533,"domain_scores_codex":[0.9993193,0.0002014747,0.00004230689,0.00009146285,0.0003061637,0.00003930366],"domain_scores_gemma":[0.9985672,0.0004807074,0.0002038781,0.00009929386,0.0005727465,0.0000761181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000940217,0.000443793,0.6040034,0.0002716653,0.0001553661,0.0003736595,0.0004079023,0.03299047,0.1179658,0.0004660072,0.0004526475,0.241529],"study_design_scores_gemma":[0.0000647395,0.002758278,0.7420327,0.0000377268,0.0001105873,0.0005609022,0.0003993766,0.2170398,0.03527441,0.0004712287,0.001181569,0.00006858295],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843508,0.0001094389,0.01444614,0.00001403163,0.000002282025,0.00007381156,0.0001994848,0.0001462878,0.0006576562],"genre_scores_gemma":[0.9752253,0.00005627755,0.02413169,0.000009404939,0.000003641237,0.00005032971,0.0002980914,0.0000137611,0.0002115721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9975677,"threshold_uncertainty_score":0.007962167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05549543246517787,"score_gpt":0.3262188852113955,"score_spread":0.2707234527462176,"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."}}