{"id":"W2053902032","doi":"10.3390/f5061356","title":"Mapping Above- and Below-Ground Biomass Components in Subtropical Forests Using Small-Footprint LiDAR","year":2014,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Lidar; Biomass (ecology); Environmental science; Subtropics; Footprint; Range (aeronautics); Tropical and subtropical moist broadleaf forests; Remote sensing; Ecology; Geography; Biology","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":[0.0004346154,0.0003890401,0.0001503427,0.0009956135,0.0001699824,0.0003355806,0.0002728132,0.0001635347,0.0002779358],"category_scores_gemma":[0.0005712912,0.0001106703,0.0002343929,0.0009791296,0.0001277985,0.0005404037,0.0003363172,0.00009971823,0.00007751711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001900228,"about_ca_system_score_gemma":0.0002014199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01011992,"about_ca_topic_score_gemma":0.02847068,"domain_scores_codex":[0.9999101,0.00002742613,0.000005298914,0.00002247437,0.00002418841,0.0000104738],"domain_scores_gemma":[0.9998152,0.00007431638,0.00002674757,0.00001914673,0.00004910135,0.00001562606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002162074,0.0001091972,0.7869219,0.0001044414,0.0001044867,0.0001974914,0.0005743453,0.0341613,0.05911891,0.0002718376,0.0001016844,0.1181182],"study_design_scores_gemma":[0.000009675319,0.00007603074,0.8470786,0.00001789059,0.00006399574,0.0001526315,0.0008207379,0.1446984,0.00614184,0.0004599101,0.0004565448,0.00002372596],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966371,0.00005790402,0.002991077,0.000006708415,6.882013e-7,0.000004352637,0.00007411591,0.00001816006,0.0002099007],"genre_scores_gemma":[0.9955473,0.00004768867,0.004183287,0.00000422783,0.000001072981,0.000004654984,0.0001456393,0.000002884287,0.00006322574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01011992,"threshold_uncertainty_score":0.02012199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02995292683585123,"score_gpt":0.2427007216460743,"score_spread":0.2127477948102231,"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."}}