{"id":"W4327563506","doi":"10.1109/migars57353.2023.10064591","title":"Enhancing Plant Area Index Retrieval Using Gaussian Process Regression from Dual-Polarimetric SAR Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Canola; Remote sensing; Backscatter (email); Entropy (arrow of time); Regression; Synthetic aperture radar; Computer science; Correlation coefficient; Gaussian process; Environmental science; Support vector machine; Gaussian; Mathematics; Artificial intelligence; Statistics; Geology; Physics; Machine learning; Agronomy","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":[0.0005880556,0.0007075348,0.0003794703,0.0005901156,0.0001356264,0.0004894242,0.0005016881,0.0004213282,0.0003958736],"category_scores_gemma":[0.0009894134,0.0001980472,0.0004803561,0.0007018834,0.0001996301,0.0007430243,0.0003880674,0.0004053264,0.0005714598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002952171,"about_ca_system_score_gemma":0.0003807378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004277207,"about_ca_topic_score_gemma":0.005469459,"domain_scores_codex":[0.9997779,0.00004133391,0.00000761592,0.00006189707,0.00008143078,0.000029939],"domain_scores_gemma":[0.9998105,0.00007517111,0.00002640083,0.00002534185,0.00005462273,0.000007982263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003104297,0.0002578892,0.01292402,0.000152901,0.0001463734,0.0002582195,0.000125831,0.520413,0.225718,0.001994415,0.001025768,0.2366732],"study_design_scores_gemma":[0.000006015285,0.00002679853,0.003081552,0.000001879325,0.00001316493,0.00003310581,0.000009671504,0.9841501,0.01204078,0.0002166467,0.000410261,0.00001005885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3088615,0.0002745741,0.6870059,0.00014463,0.00003546122,0.00003562535,0.0002176699,0.001661635,0.001763045],"genre_scores_gemma":[0.8632038,0.0003082572,0.1337398,0.00008537753,0.0000291712,0.00003623057,0.0007638059,0.0001653958,0.001668127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004277207,"threshold_uncertainty_score":0.008504629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04941551819419066,"score_gpt":0.2907987256844022,"score_spread":0.2413832074902116,"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."}}