{"id":"W4245633701","doi":"10.5194/essd-2018-79","title":"SCOPE Climate: a 142-year daily high-resolution ensemblemeteorological reconstruction dataset over France","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Climate variability and models","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Downscaling; Scope (computer science); Evapotranspiration; Climatology; Precipitation; Probabilistic logic; Environmental science; Climate model; Forcing (mathematics); Meteorology; Climate change; Homogeneous; Geography; Computer science; Artificial intelligence; Geology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008606501,0.0008157417,0.0006311469,0.001646579,0.0003752699,0.0008763532,0.001146521,0.001018548,0.005835549],"category_scores_gemma":[0.00188278,0.0002794328,0.001020355,0.002103748,0.0002542798,0.0006275662,0.0007900617,0.0008028716,0.00425043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009587726,"about_ca_system_score_gemma":0.001608564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07516348,"about_ca_topic_score_gemma":0.08120082,"domain_scores_codex":[0.999474,0.0001159148,0.00003793191,0.0001685786,0.0001279221,0.00007567699],"domain_scores_gemma":[0.999289,0.0001103987,0.00009236386,0.0001491298,0.0002860602,0.00007311818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003826405,0.000200513,0.05233095,0.0007438007,0.0008454925,0.0004716784,0.0002417157,0.03815124,0.003299955,0.003257288,0.8595705,0.04050418],"study_design_scores_gemma":[0.0008965101,0.0001293538,0.2413795,0.000358687,0.0001454184,0.0002712669,0.0003707218,0.05242543,0.002523915,0.003027531,0.6982664,0.0002051974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03190303,0.000531946,0.001674282,0.0003762261,0.00007462627,0.00005336963,0.961637,0.001824585,0.00192496],"genre_scores_gemma":[0.02754828,0.0001201279,0.002354195,0.00007425035,0.00002853611,0.00009634723,0.969017,0.0001246577,0.0006365361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07516348,"threshold_uncertainty_score":0.149452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0233736097152079,"score_gpt":0.2586757848462138,"score_spread":0.2353021751310059,"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."}}