{"id":"W3089030301","doi":"10.5194/cp-2018-133","title":"Long-term Surface Temperature (LoST) Database as a complement for GCM preindustrial simulations","year":2018,"lang":"en","type":"article","venue":"","topic":"Climate variability and models","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Canada Research Chairs; St. Francis Xavier University; U.S. Department of Energy","keywords":"Cru; GCM transcription factors; Coupled model intercomparison project; Climatology; Environmental science; Database; General Circulation Model; Climate model; Downscaling; Climate change; Transient climate simulation; Meteorology; Geography; Geology; Precipitation; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001095713,0.0005276778,0.0005131129,0.001442233,0.0003253285,0.001198086,0.001739615,0.0005500202,0.00345606],"category_scores_gemma":[0.002779853,0.0003552134,0.000628171,0.002266356,0.0001788708,0.001264881,0.0007773741,0.0007950717,0.001223587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007538256,"about_ca_system_score_gemma":0.0008695754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02467199,"about_ca_topic_score_gemma":0.01912045,"domain_scores_codex":[0.9995099,0.00009406041,0.00008142138,0.0001349744,0.0001394624,0.00004029048],"domain_scores_gemma":[0.9974598,0.0003027428,0.00034067,0.00099883,0.0007377423,0.0001602954],"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":[0.001718807,0.0006562815,0.2774866,0.001142589,0.0012573,0.0004711273,0.0004794574,0.5422881,0.01293972,0.004060165,0.08095019,0.0765496],"study_design_scores_gemma":[0.0009250517,0.0002614254,0.2923616,0.0001849667,0.0003512359,0.0002132982,0.0003519286,0.5373675,0.02275012,0.002591519,0.1423965,0.0002448895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.4245202,0.0004927339,0.02158824,0.000401551,0.0001695048,0.0002187033,0.5350574,0.007665636,0.009886043],"genre_scores_gemma":[0.6006032,0.0002225499,0.01966661,0.0001320854,0.00005470948,0.0004861866,0.3764513,0.00106571,0.001317636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02467199,"threshold_uncertainty_score":0.04905671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06437854316493753,"score_gpt":0.3271451542443495,"score_spread":0.262766611079412,"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."}}