{"id":"W4387188262","doi":"10.3390/land12101861","title":"Optimizing Soil Moisture Retrieval: Utilizing Compact Polarimetric Features with Advanced Machine Learning Techniques","year":2023,"lang":"en","type":"article","venue":"Land","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Canadian Space Agency; Environment and Climate Change Canada; Agriculture and Agri-Food Canada; Government of Canada","keywords":"Synthetic aperture radar; Remote sensing; Water content; Environmental science; Mean squared error; Polarimetry; Ground truth; Random forest; Computer science; Machine learning; Soil science; Meteorology; Mathematics; Geology; Geography; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002252092,0.0001975612,0.0002144374,0.0001274996,0.0003019675,0.00006800057,0.0001443786,0.000109332,0.00002384338],"category_scores_gemma":[0.00007617668,0.0001372128,0.00005232598,0.001240153,0.00008887022,0.0001443348,0.00009949923,0.0004702535,0.00005933531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008232713,"about_ca_system_score_gemma":0.000007948107,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006664732,"about_ca_topic_score_gemma":0.004983049,"domain_scores_codex":[0.9987525,0.00005753037,0.0001308607,0.0003371405,0.0003472677,0.0003746416],"domain_scores_gemma":[0.9995111,0.00009254289,0.00008463514,0.0002061051,0.000009543095,0.00009610086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001980961,0.00002995558,0.8984217,0.0000249908,0.00004064982,0.0001640872,0.0008283067,0.01020866,0.04566539,0.000005155351,0.000735717,0.04367728],"study_design_scores_gemma":[0.0004608887,0.0001956105,0.9589837,0.00009671037,0.00003392525,0.00008617266,0.0002258538,0.001142557,0.02513866,0.00005202302,0.0132233,0.000360564],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9279062,0.0009064576,0.0002133261,0.0005691361,0.0001372429,0.0002813878,0.000002969509,0.001091687,0.06889158],"genre_scores_gemma":[0.9938111,0.0001353583,0.00414988,0.0001487265,0.00007194959,2.818035e-7,0.0000317105,0.00004041064,0.001610551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06728102,"threshold_uncertainty_score":0.99995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009329342847383635,"score_gpt":0.2367135544100008,"score_spread":0.2273842115626172,"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."}}