{"id":"W2040646074","doi":"10.1175/jhm-d-14-0039.1","title":"Monitoring Agricultural Risk in Canada Using L-Band Passive Microwave Soil Moisture from SMOS","year":2014,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Environmental science; Water content; Anomaly (physics); Satellite; Moisture; Climatology; Flooding (psychology); Hydrology (agriculture); Meteorology; Geography; Geology","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":[0.0001593381,0.0002181366,0.000143641,0.0007270194,0.0005568834,0.0003809209,0.0003371262,0.0001002566,0.0005434453],"category_scores_gemma":[0.000410866,0.00009104091,0.0001253939,0.001042395,0.0001529373,0.0001441251,0.0002918919,0.0001259515,0.00008238034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007356453,"about_ca_system_score_gemma":0.006115867,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9823765,"about_ca_topic_score_gemma":0.9918265,"domain_scores_codex":[0.9998676,0.000008299146,0.000003775537,0.00001748426,0.00007196138,0.00003083306],"domain_scores_gemma":[0.9996688,0.00001437878,0.00004704511,0.000006012782,0.0002104597,0.00005328872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001284347,0.00005339423,0.9687349,0.00003588467,0.00004508912,0.0001322023,0.0003996841,0.002488863,0.004378418,0.0001481487,0.001342607,0.02211234],"study_design_scores_gemma":[0.000006018014,0.00002034408,0.9926313,0.00000724863,0.00001148265,0.0000150854,0.0003552724,0.005051717,0.0006762165,0.0000333729,0.001185451,0.000006457618],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953814,0.0001059374,0.0002812185,0.00007908641,0.000001869835,0.00002062911,0.002402999,0.00002656501,0.001700401],"genre_scores_gemma":[0.9970254,0.00008523586,0.0005918629,0.00002000574,0.000001597599,0.000007379036,0.001405879,0.000002669845,0.0008599942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01762354,"threshold_uncertainty_score":0.05337501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005141153098313127,"score_gpt":0.1856465709351397,"score_spread":0.1805054178368266,"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."}}