{"id":"W2891996649","doi":"10.1029/2018wr022726","title":"Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes","year":2018,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Climate variability and models","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"California Energy Commission; National Aeronautics and Space Administration; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Scale (ratio); Marginal distribution; Series (stratigraphy); A priori and a posteriori; Computer science; Bernoulli trial; Process (computing); Econometrics; Log-normal distribution; Mathematics; Statistics; Random variable; Geology; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001058835,0.00008659852,0.00008522251,0.00008619643,0.0007349374,0.0001269906,0.0001314022,0.00005468134,0.0006084823],"category_scores_gemma":[0.000289438,0.00006552923,0.00001217019,0.0001835263,0.0006864516,0.0004789671,0.0003579143,0.0000990701,0.00004714016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004796742,"about_ca_system_score_gemma":0.00001489005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003693295,"about_ca_topic_score_gemma":0.0003281776,"domain_scores_codex":[0.9986328,0.0001324556,0.0001779507,0.0003597444,0.0003828709,0.0003141193],"domain_scores_gemma":[0.9990922,0.0005103743,0.00002821499,0.0001592338,0.000105376,0.0001046021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001261579,0.0005122147,0.8376599,0.001150864,0.00005504628,0.000006186632,0.1054564,0.002358411,0.01975226,0.0005166118,0.009811003,0.02145954],"study_design_scores_gemma":[0.002890151,0.001780585,0.2328615,0.0003899432,0.00005727341,0.00004599456,0.00607952,0.2547501,0.01168442,0.1363383,0.3520956,0.001026549],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938647,0.00008558638,0.001221632,0.001412974,0.00001836309,0.0007651094,0.00005608513,0.00002464975,0.002550927],"genre_scores_gemma":[0.994143,0.00004699035,0.002781448,0.00002689287,0.0000646835,0.0001903325,0.00009515115,0.00001361406,0.002637871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6047984,"threshold_uncertainty_score":0.6662455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06910496065091305,"score_gpt":0.3452135118096057,"score_spread":0.2761085511586926,"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."}}