{"id":"W3117236311","doi":"10.1080/15481603.2020.1857123","title":"Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands","year":2020,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Remote sensing; Environmental science; Water content; Normalized Difference Vegetation Index; Synthetic aperture radar; Vegetation (pathology); Enhanced vegetation index; Radar; Leaf area index; Soil science; Vegetation Index; Geography; Geology; Agronomy; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000501489,0.0001766169,0.00019601,0.00004554302,0.0002503209,0.00006483252,0.0001919649,0.0001095852,0.00000264456],"category_scores_gemma":[0.0001305063,0.0001122112,0.00009244297,0.0004316728,0.0003449779,0.0001648827,0.00005879076,0.0001401352,0.000007233705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001487417,"about_ca_system_score_gemma":0.00002654127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002921426,"about_ca_topic_score_gemma":0.001150377,"domain_scores_codex":[0.9983264,0.00008922216,0.0003193167,0.0004370934,0.0004610855,0.0003669269],"domain_scores_gemma":[0.999468,0.00008005917,0.0001270124,0.000226671,0.00002539038,0.00007281813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001253213,0.000009304123,0.0006509703,0.0000283398,0.000001802468,0.000005802366,0.005657063,0.1111923,0.8712012,0.000001328553,0.0001181068,0.01100851],"study_design_scores_gemma":[0.0003904326,0.00006190174,0.004200662,0.00003922415,0.00001714224,0.000008954451,0.0001051632,0.6626968,0.331911,0.0002848952,0.0001455661,0.0001382294],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8846184,0.00002336177,0.1129552,0.0008369354,0.0001829212,0.0003038516,0.000001298893,0.00001912981,0.001058828],"genre_scores_gemma":[0.9867516,0.000003170821,0.01134402,0.001759858,0.00007600992,3.797231e-8,0.00001716387,0.00001637914,0.00003183337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5515046,"threshold_uncertainty_score":0.4575841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02032213806236554,"score_gpt":0.2272565584777956,"score_spread":0.2069344204154301,"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."}}