{"id":"W2550918612","doi":"10.1175/jhm-d-16-0032.1","title":"Added Value of Alternative Information in Interpolated Precipitation Datasets for Hydrology","year":2016,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Rio Tinto (Canada); Université du Québec à Chicoutimi; École de Technologie Supérieure","funders":"","keywords":"Precipitation; Environmental science; Climatology; Hydrological modelling; Meteorology; Geography; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007321694,0.00009467937,0.0002863771,0.0002855021,0.00002733091,0.000002494273,0.0002396907,0.000085457,0.0002102054],"category_scores_gemma":[0.0003279897,0.00006230332,0.00006023716,0.0001243467,0.0002145373,0.0007141512,0.0001240585,0.00008389442,0.00003422442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007510751,"about_ca_system_score_gemma":0.000006772472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000455398,"about_ca_topic_score_gemma":0.00004091706,"domain_scores_codex":[0.9988257,0.0001410902,0.0006162765,0.00009961974,0.000117383,0.0001999524],"domain_scores_gemma":[0.9989808,0.0002534211,0.0006057912,0.0001052278,0.00002208338,0.00003271547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0166214,0.001702542,0.5700229,0.0002297497,0.002455776,0.0001474806,0.01861373,0.06585778,0.1627339,0.008693377,0.05381428,0.09910709],"study_design_scores_gemma":[0.03593072,0.02391931,0.5329932,0.0003079361,0.0006159613,0.0004536144,0.0004641111,0.0343506,0.05007504,0.2119763,0.1077533,0.001159801],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913546,0.00001592715,0.005910459,0.001807231,0.0003216505,0.0001924434,0.00003673004,0.000003928013,0.0003569726],"genre_scores_gemma":[0.9983322,0.00004874908,0.001262178,0.0002889951,0.00001880408,0.00001078601,0.00001257349,0.000004093236,0.00002163495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.203283,"threshold_uncertainty_score":0.2540655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00877167174835733,"score_gpt":0.2470895500810525,"score_spread":0.2383178783326951,"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."}}