{"id":"W4379157938","doi":"10.1175/jcli-d-22-0447.1","title":"The Soil Moisture–Surface Flux Relationship as a Factor for Extreme Heat Predictability in Subseasonal to Seasonal Forecasts","year":2023,"lang":"en","type":"article","venue":"Journal of Climate","topic":"Climate variability and models","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Climate Program Office; Office of Naval Research; National Oceanic and Atmospheric Administration; Nuclear Safety and Security Commission; National Aeronautics and Space Administration; University of Miami; Environment and Climate Change Canada","keywords":"Predictability; Forecast skill; Climatology; Environmental science; Initialization; Water content; Moisture; Atmosphere (unit); Atmospheric model; Climate model; Meteorology; Atmospheric sciences; Climate change; Mathematics; Computer science; Geography; Statistics; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002600525,0.0001495968,0.0002358114,0.00004202983,0.0002923096,0.00007535356,0.0003296252,0.00009129643,0.0003783405],"category_scores_gemma":[0.001120506,0.000105179,0.0001891124,0.0004123702,0.0001083572,0.0003143092,0.0001749944,0.0002739704,0.000177316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003394745,"about_ca_system_score_gemma":0.00006466152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006327475,"about_ca_topic_score_gemma":0.0009412822,"domain_scores_codex":[0.9979472,0.0001391033,0.0005977833,0.0002340521,0.0005776227,0.0005042863],"domain_scores_gemma":[0.9982247,0.001110795,0.0001485157,0.0002316732,0.00004954766,0.0002347575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008588617,0.0001498899,0.9095434,0.00003976238,0.00001741528,0.00001104313,0.001452293,0.0796109,0.00350861,0.0005198868,0.001993906,0.002294043],"study_design_scores_gemma":[0.0007281014,0.0002210848,0.9603187,0.00005105277,0.00001721862,0.00002350176,0.0001851006,0.02510369,0.0001844972,0.007939162,0.005086096,0.0001417283],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937168,0.00003466341,0.0002258063,0.004214962,0.0002883719,0.0004223143,0.000134432,0.00002347008,0.0009391829],"genre_scores_gemma":[0.9987577,0.000072945,0.0006283739,0.0001347007,0.00007592527,0.00001831861,0.000007111591,0.00001643562,0.0002884688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05450721,"threshold_uncertainty_score":0.4289074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05659653933000922,"score_gpt":0.2955156369075592,"score_spread":0.23891909757755,"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."}}