{"id":"W3128344449","doi":"10.3390/agronomy11020273","title":"Comparison between Dense L-Band and C-Band Synthetic Aperture Radar (SAR) Time Series for Crop Area Mapping over a NISAR Calibration-Validation Site","year":2021,"lang":"en","type":"article","venue":"Agronomy","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agricultural Research Service; Nuclear Safety and Security Commission; U.S. Department of Agriculture; National Aeronautics and Space Administration","keywords":"Synthetic aperture radar; Environmental science; Crop; Remote sensing; Land cover; Vegetation (pathology); Crop yield; Grassland; Land use; Geography; Agronomy; Forestry; Ecology","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":[0.0007400034,0.0003084228,0.0001673474,0.0008629,0.0002672068,0.0004241244,0.0003670965,0.0002154743,0.0006506289],"category_scores_gemma":[0.0009719062,0.0001083915,0.0001935401,0.0009509863,0.0001369723,0.0003244222,0.0001733736,0.0001981306,0.0003442736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005285912,"about_ca_system_score_gemma":0.0004734247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04298791,"about_ca_topic_score_gemma":0.1004213,"domain_scores_codex":[0.999743,0.00003001658,0.00001355329,0.00006413406,0.00009911268,0.0000500795],"domain_scores_gemma":[0.9991579,0.000139887,0.00008616449,0.0001052432,0.0004591591,0.00005165274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009119774,0.001042133,0.5797559,0.0001424696,0.0002255576,0.0003695108,0.0006315974,0.1269369,0.08659954,0.0007554989,0.005930311,0.1966986],"study_design_scores_gemma":[0.00003521816,0.0001316732,0.806365,0.00001720182,0.00004436062,0.0000876018,0.0003179288,0.1818148,0.00879575,0.0001100433,0.002254638,0.00002584898],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906989,0.00004655754,0.005180777,0.0000204788,0.00001278159,0.00003402891,0.001512061,0.0001918722,0.002302625],"genre_scores_gemma":[0.9897294,0.00004596295,0.005361052,0.00001792413,0.000005686201,0.00002428227,0.004197249,0.00005006051,0.0005683518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04298791,"threshold_uncertainty_score":0.08547539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01426871252122064,"score_gpt":0.2290976331773378,"score_spread":0.2148289206561172,"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."}}