{"id":"W2585601568","doi":"10.14796/jwmm.c420","title":"Investigating the Spatial and Temporal Variability of Precipitation using Entropy Theory","year":2017,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Climate variability and models","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Entropy (arrow of time); Apportionment; Principle of maximum entropy; Statistical physics; Environmental science; Econometrics; Mathematics; Climatology; Statistics; Thermodynamics; Physics; Geology; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0006348814,0.0001944008,0.0001912162,0.001230139,0.000296061,0.0006314447,0.0003042357,0.0001578913,0.0003234451],"category_scores_gemma":[0.003337224,0.0001220051,0.000249823,0.0009878225,0.0004318023,0.0008185807,0.0004539674,0.0001832902,0.00003037219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001083971,"about_ca_system_score_gemma":0.0006200218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05569998,"about_ca_topic_score_gemma":0.04484076,"domain_scores_codex":[0.9998115,0.00005771627,0.00001356304,0.00003712644,0.00005264662,0.00002738435],"domain_scores_gemma":[0.9989506,0.0005983998,0.0002283988,0.0000742027,0.0001041734,0.00004412839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001045889,0.00004745023,0.6033626,0.00005967859,0.0002391382,0.0003211144,0.0007180949,0.3264061,0.006679684,0.02172807,0.0004711825,0.03986228],"study_design_scores_gemma":[0.000005286061,0.00002504859,0.2192302,0.00000627649,0.0000228417,0.00005571106,0.000190321,0.7709743,0.0008541707,0.00777897,0.0008411843,0.00001570432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9248114,0.0001830444,0.07205816,0.0001212594,0.00000734418,0.0000222476,0.0004054047,0.00004095493,0.002350073],"genre_scores_gemma":[0.9972394,0.00006460618,0.002368497,0.000004496823,0.000009095466,0.000006298076,0.0001551744,0.000003750202,0.0001486387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05569998,"threshold_uncertainty_score":0.1107515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03888079602903862,"score_gpt":0.2642403383241901,"score_spread":0.2253595422951515,"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."}}