{"id":"W3185316702","doi":"10.3390/rs13152882","title":"Using GEDI Waveforms for Improved TanDEM-X Forest Height Mapping: A Combined SINC + Legendre Approach","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Sinc function; Lidar; Remote sensing; Canopy; Legendre polynomials; Tree canopy; Environmental science; Radar; Mathematics; Computer science; Geography; Mathematical analysis","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":[0.0003015746,0.0002811617,0.0003390475,0.00005280246,0.0006026807,0.0001245931,0.0001409746,0.0001777916,0.00002314034],"category_scores_gemma":[0.00008183153,0.0002640789,0.0002023812,0.000514431,0.0001630449,0.0001467832,0.0001739832,0.0002246534,0.00003421664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000324381,"about_ca_system_score_gemma":0.00006719272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003467252,"about_ca_topic_score_gemma":0.0001155845,"domain_scores_codex":[0.9980022,0.00005924207,0.0003957957,0.0006858027,0.0002499507,0.0006070522],"domain_scores_gemma":[0.9988778,0.00007462478,0.0001641124,0.0006309971,0.00006128329,0.0001912082],"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":[0.00004810406,0.0001560849,0.0003044823,0.00008157496,0.0001129828,0.00003628783,0.001724199,0.00987897,0.7207007,0.0002603586,0.0009664066,0.2657298],"study_design_scores_gemma":[0.0006914918,0.00002760555,0.0005837426,0.00004255736,0.00004468164,0.0003172966,0.0003849399,0.9722266,0.008902257,0.001870984,0.01453594,0.0003718698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3143865,0.00005215853,0.6690164,0.0005414672,0.0002264207,0.0005925975,0.000006711642,0.0001425743,0.01503515],"genre_scores_gemma":[0.6336186,0.000009770964,0.3647405,0.0003389075,0.0001876788,5.347549e-8,0.00008165652,0.00005707594,0.0009657477],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9623477,"threshold_uncertainty_score":0.9999812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0347689054698266,"score_gpt":0.2520745676006618,"score_spread":0.2173056621308352,"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."}}