{"id":"W2792812251","doi":"10.1109/lgrs.2018.2791858","title":"A Tool for Analysis and Calibration of Compact Polarimetry SAR Mode Anomaly","year":2018,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Polarimetry; Synthetic aperture radar; Calibration; Remote sensing; Polarization (electrochemistry); Mode (computer interface); Side looking airborne radar; Computer science; Radar; Physics; Radar imaging; Optics; Bistatic radar; Geology; Scattering; Telecommunications","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":[0.0001206749,0.00008762042,0.0001545442,0.0001622616,0.0001084786,0.0000330925,0.00004845399,0.00004488731,6.579746e-7],"category_scores_gemma":[0.00001161152,0.00007594403,0.00004088933,0.000351291,0.0002266055,0.00006381669,0.000006462738,0.00004012206,1.520757e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001302789,"about_ca_system_score_gemma":0.000006501069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004571521,"about_ca_topic_score_gemma":0.0000202647,"domain_scores_codex":[0.9994639,0.000009305586,0.0001352466,0.0001685294,0.0000846355,0.0001384408],"domain_scores_gemma":[0.9996896,0.00005692569,0.00003557142,0.0001568613,0.000027138,0.00003384498],"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":[0.000008431261,0.000006346156,0.0005114883,0.00003971099,0.00009112206,8.653977e-7,0.0004094276,0.00008910635,0.4853818,0.00005700349,0.0003294514,0.5130753],"study_design_scores_gemma":[0.0000674172,0.00002394627,0.002043311,0.00001888906,0.00008828223,0.000008681642,0.00001940343,0.8880931,0.106755,0.0001308651,0.002633427,0.0001176693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4549115,0.00001931255,0.5446097,0.0002820427,0.00003487094,0.00006572781,0.000005412145,0.00004063746,0.0000307752],"genre_scores_gemma":[0.6546299,0.000008895637,0.3451064,0.0002004081,0.00004122188,7.024937e-9,0.000001510336,0.000005841145,0.000005815605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.888004,"threshold_uncertainty_score":0.3096908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01003847394949488,"score_gpt":0.2448516531893902,"score_spread":0.2348131792398953,"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."}}