{"id":"W2911799365","doi":"10.3390/rs11040381","title":"Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Lidar; Coherence (philosophical gambling strategy); Interferometry; Polarimetry; Interferometric synthetic aperture radar; Canopy; Synthetic aperture radar; Waveform; Environmental science; Computer science; Radar; Geology; Scattering; Optics; Geography; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001479717,0.0002030585,0.0002877062,0.00003508671,0.0001675057,0.00004983555,0.0003060358,0.0001647726,0.0001296045],"category_scores_gemma":[0.0001448001,0.000190118,0.0000559902,0.0002722873,0.000168491,0.0001461616,0.0003971059,0.0004308352,0.00006244787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001067766,"about_ca_system_score_gemma":0.00002274514,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007325462,"about_ca_topic_score_gemma":0.0002653845,"domain_scores_codex":[0.9983025,0.0001108607,0.0002846776,0.0005704893,0.0004068769,0.0003245511],"domain_scores_gemma":[0.9987009,0.0001277041,0.000118032,0.0009188337,0.00001740293,0.0001171863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004845345,0.00000892716,0.002238695,0.000006298947,0.00002241716,0.000003516775,0.000193538,0.004028758,0.9754798,0.000002076154,0.0001575906,0.01780991],"study_design_scores_gemma":[0.0004322178,0.0000345667,0.008529498,0.00007237246,0.00006483384,0.00005111166,0.000100779,0.8908421,0.09584568,0.0003424206,0.003390379,0.0002940467],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913558,0.0001410262,0.006927467,0.0001696554,0.0001310037,0.0001751198,0.00007623958,0.00005797038,0.0009656567],"genre_scores_gemma":[0.9628002,0.00001541191,0.03652024,0.00009293007,0.00008606764,4.171289e-10,0.0003190123,0.00003909091,0.0001270293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8868133,"threshold_uncertainty_score":0.9992849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01506652384711619,"score_gpt":0.2496980002034251,"score_spread":0.2346314763563089,"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."}}