{"id":"W2099300481","doi":"10.1002/2014jd022511","title":"Climate coupling between temperature, humidity, precipitation, and cloud cover over the Canadian Prairies","year":2014,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Climate variability and models","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Natural Resources Canada; George Mason University; National Science Foundation","keywords":"Environmental science; Precipitation; Cloud cover; Climatology; Relative humidity; Atmospheric sciences; Climate model; Humidity; Evapotranspiration; Forcing (mathematics); Cloud forcing; Climate change; Meteorology; Geography; Cloud computing; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003148821,0.0001170806,0.0002270977,0.00001233755,0.0008512295,0.000285403,0.0003764869,0.00009320823,0.0003780253],"category_scores_gemma":[0.001115219,0.00007374596,0.00007460479,0.0002694816,0.000825987,0.000378821,0.0002609161,0.0007463375,0.0001011256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000228409,"about_ca_system_score_gemma":0.0001282537,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05183097,"about_ca_topic_score_gemma":0.05789634,"domain_scores_codex":[0.9977188,0.0002827848,0.0003063501,0.0001950292,0.000974234,0.0005227624],"domain_scores_gemma":[0.9978344,0.001308011,0.0001145085,0.0002238217,0.0001285158,0.0003907603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003060579,0.0003062664,0.8955737,0.0001293038,0.0001513195,0.00002394463,0.002489211,0.01013012,0.005074949,0.01355363,0.0595223,0.01273917],"study_design_scores_gemma":[0.0003383336,0.0003635581,0.950392,0.00005250983,0.00002163981,0.000005240673,0.0001032307,0.003953451,0.0003130212,0.02153249,0.0227985,0.0001260136],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952581,0.00004562937,0.00003443052,0.002449878,0.0001130872,0.0001624998,0.00001193289,0.000005164552,0.001919275],"genre_scores_gemma":[0.9986256,0.00007495291,0.0003010572,0.000126543,0.0006740513,0.000003921133,0.000001988704,0.00001264677,0.000179258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05481829,"threshold_uncertainty_score":0.9592946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03294761389729979,"score_gpt":0.3159898043364415,"score_spread":0.2830421904391417,"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."}}