{"id":"W4306835720","doi":"10.3390/rs14205234","title":"Improved Model-Based Forest Height Inversion Using Airborne L-Band Repeat-Pass Dual-Baseline Pol-InSAR Data","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing Institute of Technology; Beijing Institute of Technology Research Fund Program for Young Scholars; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Remote sensing; Attenuation; Inversion (geology); Interferometric synthetic aperture radar; Environmental science; Synthetic aperture radar; Mean squared error; Geology; Computer science; Geodesy; Mathematics; Physics; Optics; Tectonics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004798037,0.0002816631,0.0002886804,0.0001838069,0.0004279419,0.00004124607,0.0002890522,0.0001061124,0.00001925407],"category_scores_gemma":[0.00005275845,0.0003019153,0.00008197645,0.0003814569,0.00005423271,0.0001215734,0.0002155706,0.0003693134,0.000002697605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003204314,"about_ca_system_score_gemma":0.00009758201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002840595,"about_ca_topic_score_gemma":0.00003549461,"domain_scores_codex":[0.9983642,0.00006939608,0.0003757344,0.000518249,0.0002922857,0.0003801144],"domain_scores_gemma":[0.9981871,0.000107229,0.00009313584,0.001440868,0.00006148888,0.0001102291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005197115,0.00005423173,0.000007482858,0.0001088891,0.00007571137,0.00006314497,0.0001316088,0.1078056,0.1156044,0.00007660392,0.002391871,0.7736285],"study_design_scores_gemma":[0.0003113597,0.00002070908,0.000002356616,0.00004996273,0.00005883721,0.00006466466,0.00004382384,0.9103199,0.02966768,0.0002839478,0.0588571,0.0003196262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05681123,0.0003037932,0.9404408,0.0004831723,0.0001646291,0.0003573066,0.00009787078,0.0007842366,0.0005569023],"genre_scores_gemma":[0.5426104,0.00002012749,0.4567578,0.0001905728,0.0001023533,7.94851e-8,0.0002181701,0.00007689688,0.00002355586],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8025144,"threshold_uncertainty_score":0.9999433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02427890570016498,"score_gpt":0.242517649482264,"score_spread":0.2182387437820991,"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."}}