{"id":"W6889633875","doi":"10.26023/7a7k-66c1-y60a","title":"CEOP/EOP-1: MAGS: BERMS Hourly Surface Meteorology, Flux, and Radiation Data Set. Version 1.0","year":2011,"lang":"en","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Water cycle; Radiation; Data set; Surface water; Albedo (alchemy); Boreal; Flux (metallurgy); Period (music)","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":[0.0007832955,0.001698792,0.001253034,0.002148411,0.0005029984,0.00139701,0.002812232,0.001645482,0.02454076],"category_scores_gemma":[0.003237405,0.0007771138,0.0009015395,0.005144429,0.0002777554,0.001260495,0.001062225,0.001657394,0.03732231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271809,"about_ca_system_score_gemma":0.001842237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05072753,"about_ca_topic_score_gemma":0.06184101,"domain_scores_codex":[0.9993962,0.00008025963,0.00008744304,0.0001590768,0.0001658422,0.0001111355],"domain_scores_gemma":[0.9984855,0.0002504295,0.0002495695,0.0003067812,0.0005453539,0.0001623217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005137081,0.00002977521,0.001823912,0.0002538381,0.00002786542,0.00001927661,0.0000251375,0.0004988835,0.0001022936,0.0002303233,0.9954506,0.00148667],"study_design_scores_gemma":[0.0004986521,0.00003725701,0.03813443,0.0002523854,0.00005739354,0.0000977719,0.0002159821,0.002320166,0.001049218,0.001189162,0.9560673,0.00008029576],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003129079,0.00001595695,0.00004249255,0.00002869158,0.00001233164,0.000006658957,0.9992349,0.000137137,0.0002089318],"genre_scores_gemma":[0.0005538479,0.00001220346,0.0001396183,0.0000128687,0.000003966291,0.00003356973,0.9989678,0.00002734366,0.00024893],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05072753,"threshold_uncertainty_score":0.1008645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08692769641139549,"score_gpt":0.338091050738747,"score_spread":0.2511633543273515,"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."}}