{"id":"W2946576669","doi":"10.3390/data4020072","title":"Climate Data to Undertake Hygrothermal and Whole Building Simulations Under Projected Climate Change Influences for 11 Canadian Cities","year":2019,"lang":"en","type":"article","venue":"Data","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada; Environment and Climate Change Canada","keywords":"Downscaling; Environmental science; Climate change; Cloud cover; Climate model; Precipitation; Climatology; Global warming; Range (aeronautics); Meteorology; Wind speed; Relative humidity; Snow; Geography; Computer science; Cloud computing; Engineering","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":[0.0001200109,0.0001100764,0.0001057154,0.0001239375,0.0001334666,0.0001037714,0.0004573021,0.00005933782,0.00002145459],"category_scores_gemma":[0.0000168101,0.0001140694,0.000006561412,0.0001528072,0.00001297416,0.0007519783,0.0002440449,0.00004839537,0.000004055373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002563928,"about_ca_system_score_gemma":0.00002394677,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004248633,"about_ca_topic_score_gemma":0.104387,"domain_scores_codex":[0.9992182,0.000008362368,0.0001277403,0.0002689128,0.00006584294,0.0003109617],"domain_scores_gemma":[0.9990571,0.00004950563,0.00001952597,0.000770931,0.00001943665,0.00008351333],"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":[0.00001218648,0.000008184521,0.01464382,0.0001969106,0.00004768531,8.389726e-7,0.0002689548,0.9725809,0.00105756,0.006307328,0.001837872,0.003037747],"study_design_scores_gemma":[0.0001997269,0.00001332863,0.004838461,0.00007445783,0.00002132513,0.000001465206,0.0000985493,0.9640844,0.00004427095,0.00007254867,0.030349,0.0002024986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9690642,0.0002156646,0.007990904,0.0006274839,0.0004246297,0.0009752542,0.02017783,0.0003083589,0.0002156505],"genre_scores_gemma":[0.984753,0.0000664646,0.007255173,0.0003887028,0.00008451355,0.0000289325,0.007369465,0.00003656241,0.00001718338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1001384,"threshold_uncertainty_score":0.9119556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09568365112707507,"score_gpt":0.2932047752567594,"score_spread":0.1975211241296844,"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."}}