{"id":"W2025613300","doi":"10.1080/02626667.2014.966719","title":"Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices","year":2014,"lang":"en","type":"article","venue":"Hydrological Sciences Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cégep Garneau; Hydro-Québec; Institut National de la Recherche Scientifique","funders":"","keywords":"Downscaling; Environmental science; Precipitation; Bayesian probability; Climatology; Flow (mathematics); Generalization; Meteorology; Statistics; Econometrics; Computer science; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005078885,0.0004966668,0.0004508303,0.0008155404,0.000308498,0.0008871137,0.0008958414,0.0004186919,0.0008770386],"category_scores_gemma":[0.01806907,0.0003848123,0.0005477277,0.0006804824,0.0004650007,0.001170562,0.001070301,0.000681438,0.0001521852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006677592,"about_ca_system_score_gemma":0.001527072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01475048,"about_ca_topic_score_gemma":0.02229656,"domain_scores_codex":[0.9985632,0.0007353196,0.00009809127,0.00017724,0.0003580066,0.00006824555],"domain_scores_gemma":[0.9931791,0.004182375,0.0005807737,0.0005489535,0.001405173,0.0001037922],"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":[0.0002578949,0.0001317463,0.01496159,0.0001019726,0.0002450204,0.00003035918,0.0001645034,0.7318996,0.001401129,0.005744838,0.0004579177,0.2446035],"study_design_scores_gemma":[0.00002720306,0.00004938755,0.002139998,0.00001025521,0.00002279994,0.00001232251,0.00001838169,0.9956279,0.0007451728,0.001097699,0.0002396233,0.000009255941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2481203,0.0006647471,0.747023,0.0004684698,0.00005643042,0.0001645641,0.0001871804,0.0006534663,0.002661817],"genre_scores_gemma":[0.7630001,0.0002727599,0.2352564,0.00009189977,0.00005528221,0.0001400926,0.0003290768,0.0001088507,0.0007455572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01475048,"threshold_uncertainty_score":0.02932924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02019379768503332,"score_gpt":0.297140207815861,"score_spread":0.2769464101308277,"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."}}