{"id":"W3018431463","doi":"10.12000/jr20009","title":"Evaluating the Impacts of Using Different Digital Surface Models to Estimate Forest Height with TanDEM-X Interferometric Coherence Data","year":2020,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service","funders":"","keywords":"Coherence (philosophical gambling strategy); Remote sensing; Interferometry; Digital surface; Tandem; Digital elevation model; Environmental science; Computer science; Geology; Optics; Mathematics; Physics; Statistics; Materials science; Lidar","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004379913,0.00120923,0.0005271479,0.001005745,0.0006205025,0.001188385,0.001187224,0.001000848,0.0004335141],"category_scores_gemma":[0.009376516,0.0005151559,0.0008830471,0.001186549,0.0005330764,0.001565936,0.0009968067,0.0005959146,0.0001723328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002011225,"about_ca_system_score_gemma":0.001297102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1357217,"about_ca_topic_score_gemma":0.09856528,"domain_scores_codex":[0.9986748,0.0003631159,0.00009615597,0.0002934484,0.0004073747,0.000165163],"domain_scores_gemma":[0.9951231,0.002645952,0.0004392606,0.0004935293,0.001155783,0.0001423437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001231668,0.0004486903,0.1385887,0.0001676962,0.0005835848,0.0002222503,0.0001715187,0.7964448,0.01141014,0.0005157304,0.0003433494,0.04987177],"study_design_scores_gemma":[0.00009021723,0.0004029669,0.06335328,0.00002075764,0.0001547347,0.0000702579,0.0001788659,0.9254361,0.009700608,0.0001731813,0.0003459703,0.00007307847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843146,0.0002062048,0.01367159,0.00009552638,0.00002311309,0.00005920455,0.0004937156,0.0003545433,0.0007815366],"genre_scores_gemma":[0.9823422,0.00009928056,0.01646166,0.00004845146,0.000006511509,0.00003044994,0.0007632811,0.00004090163,0.0002071947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1357217,"threshold_uncertainty_score":0.2698634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4768779965905657,"score_gpt":0.557065565142386,"score_spread":0.08018756855182035,"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."}}