{"id":"W2751248724","doi":"10.1002/2017ms001025","title":"Analysis of near‐surface biases in <scp>ERA</scp>‐<scp>I</scp>nterim over the <scp>C</scp>anadian <scp>P</scp>rairies","year":2017,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Climate variability and models","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Vermont; National Science Foundation","keywords":"Environmental science; Diurnal cycle; Atmospheric sciences; Diurnal temperature variation; Snow; Daytime; Climatology; Forcing (mathematics); Meteorology; Physics; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004708734,0.0006870823,0.001770303,0.0004943665,0.0007508183,0.0007209392,0.001976337,0.0004082224,0.00002086278],"category_scores_gemma":[0.01029001,0.0005471913,0.0006964144,0.001123894,0.0008640004,0.002840123,0.0004895231,0.001179771,0.00004421704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003699359,"about_ca_system_score_gemma":0.0002346538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004930009,"about_ca_topic_score_gemma":0.01331041,"domain_scores_codex":[0.9928719,0.0006300721,0.002544022,0.0008917235,0.001719968,0.001342319],"domain_scores_gemma":[0.9892219,0.006025357,0.00249506,0.001546751,0.0002133877,0.0004975281],"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":[0.000005380202,0.0001805025,0.1319602,0.0001364252,0.000202272,0.00005717378,0.007071911,0.8587099,0.0009697711,0.00005362906,0.0004517816,0.0002011364],"study_design_scores_gemma":[0.001214191,0.0002715868,0.02903865,0.001081262,0.0003676318,0.00007753124,0.01646715,0.9319483,0.0003673721,0.0006729836,0.0183908,0.0001025362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843041,0.006357318,0.003522211,0.00007739963,0.001182193,0.0005956878,0.000149937,0.00003613953,0.003774992],"genre_scores_gemma":[0.9937769,0.00322094,0.001567464,0.0001384629,0.0002356191,0.00002001042,0.00001319479,0.00006959987,0.0009578565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1029215,"threshold_uncertainty_score":0.999698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03168208117314095,"score_gpt":0.2870154254119867,"score_spread":0.2553333442388457,"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."}}