{"id":"W2038114921","doi":"10.1175/waf-d-11-00125.1","title":"Integrating NWP Forecasts and Observation Data to Improve Nowcasting Accuracy","year":2012,"lang":"en","type":"article","venue":"Weather and Forecasting","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Environment and Climate Change Canada","funders":"Institut National Du Cancer","keywords":"Nowcasting; Weighting; Numerical weather prediction; Meteorology; Computer science; Environmental science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007696865,0.0001309141,0.0001510678,0.00004738185,0.0003068369,0.0001029481,0.0001352845,0.00004918487,0.0001857856],"category_scores_gemma":[0.001094936,0.00009573896,0.00001469024,0.0001441583,0.00003256711,0.0007185362,0.00008395704,0.000119637,0.00001560026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002533127,"about_ca_system_score_gemma":0.000007538292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003516839,"about_ca_topic_score_gemma":0.0002544267,"domain_scores_codex":[0.9989634,0.00004681983,0.0002443409,0.0002758699,0.0001145583,0.0003550444],"domain_scores_gemma":[0.9987674,0.0007171707,0.00008723732,0.0001875639,0.0000278708,0.0002127463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001104943,0.000004777972,0.5377986,0.00001151589,0.000005909364,4.36946e-7,0.0006979394,0.0001631605,0.0003190572,0.0001486209,0.00001631284,0.4608227],"study_design_scores_gemma":[0.0002035633,0.0001081382,0.5977715,0.00003096366,0.00001788021,0.0000138443,0.0004949102,0.3985975,0.00001615946,0.0008080571,0.001729023,0.0002085275],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895586,0.0004806048,0.002877422,0.0001096874,0.0001726604,0.00019579,0.00006176791,0.00003512817,0.006508335],"genre_scores_gemma":[0.9781808,0.000006547785,0.02102731,0.0002680345,0.0003267878,0.000001629534,0.00009405892,0.000004845092,0.00009000274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4606141,"threshold_uncertainty_score":0.3904122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1642831461458056,"score_gpt":0.2857894429045302,"score_spread":0.1215062967587247,"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."}}