{"id":"W2923438260","doi":"10.1016/j.crte.2018.11.005","title":"Optimization approach to retrieve soil surface parameters from single-acquisition single-configuration SAR data","year":2019,"lang":"en","type":"article","venue":"Comptes Rendus Géoscience","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Synthetic aperture radar; Water content; Surface roughness; Environmental science; Mean squared error; Ground truth; Data acquisition; Soil science; Computer science; Geology; Materials science; Mathematics; Artificial intelligence; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006151139,0.0009476909,0.0008144465,0.0007556245,0.0002435868,0.0005900032,0.0006843634,0.0006297985,0.0007068382],"category_scores_gemma":[0.001006301,0.0004403136,0.0007762622,0.0009212155,0.0003869178,0.0005881234,0.0004001144,0.0004481345,0.0001875977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004905825,"about_ca_system_score_gemma":0.001122106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006734972,"about_ca_topic_score_gemma":0.005096493,"domain_scores_codex":[0.9997422,0.00007197259,0.00001791765,0.00006452271,0.00007528866,0.00002806102],"domain_scores_gemma":[0.9997424,0.0001191304,0.00004257312,0.00001966708,0.00006696492,0.000009222093],"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":[0.00002067975,0.00002863798,0.0005352859,0.00004071357,0.00004544362,0.00002990977,0.00001938708,0.9645423,0.003159457,0.0009669879,0.0001972167,0.03041402],"study_design_scores_gemma":[0.000004557433,0.00001714242,0.000219286,0.0000022082,0.000006850403,0.000007354065,0.000006527866,0.99852,0.0006779756,0.0003998119,0.0001339915,0.00000439295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03439974,0.000188822,0.9639105,0.00007852125,0.00001085793,0.00003676337,0.00004842964,0.0002036581,0.001122758],"genre_scores_gemma":[0.499041,0.0003410674,0.4982226,0.00008178851,0.00002697654,0.0002464163,0.0002886293,0.0001044944,0.001647137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006734972,"threshold_uncertainty_score":0.01339155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04283467300454025,"score_gpt":0.2274271047147635,"score_spread":0.1845924317102232,"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."}}