{"id":"W3122078438","doi":"10.20944/preprints201811.0217.v1","title":"Understanding Land-Atmosphere-Climate Coupling from the Canadian Prairie Dataset","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Climate variability and models","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Environmental science; Cloud cover; Cloud forcing; Atmospheric sciences; Snow; Forcing (mathematics); Longwave; Climatology; Atmosphere (unit); Radiative forcing; Opacity; Radiative transfer; Meteorology; Cloud computing; Geography; Geology; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001036871,0.000972341,0.0006312437,0.003270537,0.001457831,0.001639149,0.001897558,0.0007012656,0.002854002],"category_scores_gemma":[0.003856268,0.0004653811,0.001053731,0.00808649,0.0003728307,0.0007870484,0.00126501,0.00110537,0.001245816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01236417,"about_ca_system_score_gemma":0.01953184,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9931831,"about_ca_topic_score_gemma":0.9958593,"domain_scores_codex":[0.9989049,0.00007205484,0.00005189223,0.0002370776,0.0004543599,0.0002795338],"domain_scores_gemma":[0.9974107,0.0001597433,0.0001797969,0.0002407348,0.001817171,0.0001918563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003203786,0.0001819129,0.3832942,0.001461179,0.001189232,0.0005172488,0.001266092,0.03998474,0.003847311,0.006335212,0.4793361,0.08226637],"study_design_scores_gemma":[0.0001022978,0.00002003553,0.7615638,0.0002599726,0.0001722983,0.00006898182,0.0009837075,0.04190778,0.0009949083,0.001098455,0.1926297,0.0001980822],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1179883,0.001478488,0.002207061,0.001145781,0.0001026026,0.0001590059,0.8678864,0.0008508615,0.008181622],"genre_scores_gemma":[0.1819967,0.001014901,0.009455544,0.0002335447,0.00003961744,0.000183927,0.8041787,0.0001443159,0.002752678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01236417,"threshold_uncertainty_score":0.08970869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2529690193940924,"score_gpt":0.3317913372322158,"score_spread":0.07882231783812332,"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."}}