{"id":"W4399465205","doi":"10.1186/s13007-024-01212-4","title":"Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia","year":2024,"lang":"en","type":"article","venue":"Plant Methods","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Foundation; National Science and Technology Council; International Development Research Centre; United States Agency for International Development; U.S. Department of Agriculture","keywords":"Sampling (signal processing); Lidar; Satellite; Environmental science; Remote sensing; Woodland; Geography; Computer science; Astronomy; Physics; Biology; Ecology; Detector","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0005966227,0.0002873468,0.0001776779,0.001346398,0.0003667413,0.0005911376,0.0003418516,0.0002084094,0.0004553617],"category_scores_gemma":[0.001094474,0.000194703,0.0001631237,0.001093072,0.0002102544,0.0004658583,0.0004976926,0.0001899975,0.00009905766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005559564,"about_ca_system_score_gemma":0.000493585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06088343,"about_ca_topic_score_gemma":0.1722119,"domain_scores_codex":[0.9997485,0.00009554797,0.0000171057,0.00003522,0.00005548223,0.00004798139],"domain_scores_gemma":[0.9996588,0.00008374394,0.00009349318,0.00002821888,0.0001187688,0.00001684532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001455844,0.0001148892,0.9062911,0.0002547638,0.00006484367,0.0003940478,0.002181735,0.005116642,0.01862218,0.0004161659,0.0002503414,0.06614771],"study_design_scores_gemma":[0.00001102872,0.0001590758,0.9658618,0.0001503424,0.00006322663,0.0001387267,0.005096268,0.02377744,0.003056465,0.0001032911,0.001564957,0.00001728401],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956131,0.0002847468,0.002787124,0.00004647752,0.000003191559,0.00006941424,0.0002387638,0.00001852322,0.0009385674],"genre_scores_gemma":[0.9930778,0.0001411231,0.006424865,0.00001222993,0.000001746847,0.00004960416,0.0001476042,0.000001987806,0.0001430918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06088343,"threshold_uncertainty_score":0.1210581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1745610096296136,"score_gpt":0.3876632676839053,"score_spread":0.2131022580542917,"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."}}