{"id":"W2548447597","doi":"10.1109/igarss.2016.7730363","title":"Impacts of SMAP data in Environment Canada's Regional Deterministic Prediction System","year":2016,"lang":"en","type":"article","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Data assimilation; Environmental science; Context (archaeology); Meteorology; Remote sensing; Brightness; Atmospheric model; Brightness temperature; Computer science; Climatology; Geology; Geography","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":[0.004513532,0.0008464197,0.0005961114,0.0004663845,0.001955374,0.002359173,0.00176862,0.0008129478,0.001112751],"category_scores_gemma":[0.0136145,0.0004837677,0.0005943389,0.001601944,0.0009472787,0.001129383,0.001362441,0.001186631,0.0002233069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01435507,"about_ca_system_score_gemma":0.02246422,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9630983,"about_ca_topic_score_gemma":0.945493,"domain_scores_codex":[0.9971125,0.0008550216,0.0001383559,0.0005251257,0.001051458,0.0003175411],"domain_scores_gemma":[0.9932492,0.001851883,0.0003704039,0.0005797049,0.003592016,0.0003567935],"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":[0.0009128604,0.0002070265,0.1037998,0.0001352794,0.0002925119,0.0001811123,0.0001332642,0.8518,0.002464064,0.00421981,0.009543573,0.0263107],"study_design_scores_gemma":[0.0002560454,0.00009988411,0.07565264,0.00004492055,0.0001265656,0.00002183623,0.0002076323,0.9144614,0.002836199,0.001113213,0.005067293,0.0001124146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9462656,0.0008975168,0.009798809,0.004948334,0.0003758802,0.000235456,0.02053251,0.001802538,0.01514327],"genre_scores_gemma":[0.9841385,0.0002445241,0.007829055,0.0002860483,0.00002163977,0.00004028386,0.006090661,0.00007812411,0.001271097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03690171,"threshold_uncertainty_score":0.1041538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01477050100705017,"score_gpt":0.1982574057201394,"score_spread":0.1834869047130892,"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."}}