{"id":"W4388264175","doi":"10.3390/rs15215110","title":"Evaluating SAR Radiometric Terrain Correction Products: Analysis-Ready Data for Users","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Remote sensing; Terrain; Environmental science; Synthetic aperture radar; Geolocation; Radiometric calibration; Earth observation; Computer science; Software; Radiometric dating; Satellite; Calibration; Geology; Geography; Aerospace engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0009375004,0.0001587112,0.0002493594,0.0008663633,0.0001455672,0.00005245295,0.0001852209,0.00008505183,0.000003299179],"category_scores_gemma":[0.0006818454,0.0001609579,0.00006307649,0.004289435,0.00002254602,0.0001012477,0.00005349351,0.0001175785,0.00001655583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001226821,"about_ca_system_score_gemma":0.00002219562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001095065,"about_ca_topic_score_gemma":0.00001771739,"domain_scores_codex":[0.9987917,0.00003506696,0.0002765162,0.0004091711,0.0002060876,0.0002814533],"domain_scores_gemma":[0.9985201,0.000311215,0.00006176957,0.0009666407,0.00009554002,0.00004472835],"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":[0.000002623615,0.00000317579,0.000004268051,0.00003214934,0.0001696892,0.000001052513,0.00009816077,0.0009991552,0.005597821,0.000005510566,0.004147063,0.9889393],"study_design_scores_gemma":[0.00008222784,0.00001421851,0.00008433973,0.00002848227,0.0002648093,0.00001345207,0.0000923112,0.8754109,0.005287312,0.00008712833,0.1184716,0.0001631841],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02995136,0.0001626726,0.9665135,0.0002432555,0.0004143161,0.0005289031,0.00002777967,0.001505863,0.0006523185],"genre_scores_gemma":[0.1808382,0.00007259029,0.8178502,0.000027557,0.0002868923,2.088447e-7,0.0006598662,0.0000671535,0.0001973023],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9887761,"threshold_uncertainty_score":0.6563674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07788389528839434,"score_gpt":0.3457403823455481,"score_spread":0.2678564870571538,"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."}}