{"id":"W2969571298","doi":"10.3390/rs11161956","title":"Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Western University","funders":"National Natural Science Foundation of China","keywords":"Remote sensing; Environmental science; Synthetic aperture radar; Vegetation (pathology); Growing season; Water content; Backscatter (email); Meteorology; Soil science; Geology; Agronomy; Geography; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004275493,0.0004511753,0.0005179011,0.00005040504,0.0004635449,0.0002071185,0.0003296428,0.0003662878,0.00002868032],"category_scores_gemma":[0.00004580568,0.0003799883,0.0001003316,0.0001873967,0.0001286381,0.0008954629,0.0012142,0.0006122782,0.00004559929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002514141,"about_ca_system_score_gemma":0.00002259822,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01204548,"about_ca_topic_score_gemma":0.0007089908,"domain_scores_codex":[0.996846,0.0001347813,0.0004125345,0.001202288,0.0006132941,0.0007911656],"domain_scores_gemma":[0.9982424,0.0001006187,0.0001232435,0.001306111,0.00001823484,0.0002093554],"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":[0.00008971583,0.00001123031,0.00269875,0.00005226178,0.00006131275,0.0000959415,0.002079714,0.2635438,0.7095391,0.000001276411,0.00008223872,0.02174466],"study_design_scores_gemma":[0.0006962219,0.00001096766,0.002077165,0.0002879934,0.00008896569,0.00006470573,0.0003521793,0.9478477,0.04754813,0.0003410267,0.0001260391,0.0005588761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878018,0.0002778997,0.007603066,0.0002588163,0.0006104169,0.0002711345,0.000007450322,0.0001295069,0.003039923],"genre_scores_gemma":[0.9732055,0.00005410205,0.02560159,0.0003421985,0.0003422835,2.796721e-10,0.00005240922,0.00008889284,0.000313001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6843039,"threshold_uncertainty_score":0.9998652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01947388124681162,"score_gpt":0.2293725150286321,"score_spread":0.2098986337818205,"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."}}