{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008274297,0.001404601,0.0007105695,0.002422899,0.000530015,0.002163327,0.001281068,0.0007609219,0.007396048],"category_scores_gemma":[0.02524955,0.0005693123,0.0007328267,0.001798847,0.000455072,0.003207887,0.001946123,0.0007884931,0.007573674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006685902,"about_ca_system_score_gemma":0.0009087334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005896366,"about_ca_topic_score_gemma":0.005577277,"domain_scores_codex":[0.9957371,0.0008270523,0.0003976184,0.0006408261,0.002176174,0.0002212666],"domain_scores_gemma":[0.9819831,0.003624329,0.00116386,0.003402466,0.009316385,0.0005099228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004681403,0.002164156,0.1372965,0.001996262,0.0005790141,0.0009070473,0.003559874,0.02811809,0.05847627,0.006809937,0.1846682,0.5707432],"study_design_scores_gemma":[0.001069097,0.002428153,0.2807522,0.001155938,0.0005638847,0.001127242,0.004813349,0.2012696,0.1649193,0.008691393,0.3326008,0.0006089766],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5877857,0.0007592592,0.1878327,0.0009308619,0.000321844,0.003504321,0.1100438,0.06519779,0.04362372],"genre_scores_gemma":[0.4965226,0.0004260465,0.2957794,0.000501263,0.00009314256,0.00259027,0.1793964,0.01570291,0.0089879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008274297,"threshold_uncertainty_score":0.04375923,"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."}}