{"id":"W1537641517","doi":"10.1002/2012wr013442","title":"An efficient framework for hydrologic model calibration on long data periods","year":2013,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Calibration; Representativeness heuristic; Hydrological modelling; Computer science; Function (biology); Range (aeronautics); Data mining; Statistics; Mathematics; Engineering; Geology","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001337815,0.0001349085,0.0001307534,0.00009158414,0.0007551687,0.0001547359,0.001025468,0.0001220133,0.00107089],"category_scores_gemma":[0.00006846285,0.00008373416,0.00002721696,0.0001163085,0.0004628147,0.000261789,0.001213817,0.0002949583,0.001126206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000055546,"about_ca_system_score_gemma":0.000002026898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002582133,"about_ca_topic_score_gemma":0.00003095321,"domain_scores_codex":[0.9977097,0.000235049,0.0001626485,0.0006665319,0.0005024548,0.0007236178],"domain_scores_gemma":[0.9988083,0.0001113122,0.00001934527,0.0009341788,0.00001304202,0.0001138494],"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":[0.0002329973,0.000464024,0.02416936,0.00003746166,0.00004537473,0.000009992561,0.009672288,0.949444,0.00330159,0.0005473035,0.009496442,0.002579177],"study_design_scores_gemma":[0.0001652099,0.0003322601,0.00244498,0.000006543339,0.00000554761,4.487763e-7,0.0001513067,0.9821137,0.001201419,0.008745715,0.004700577,0.0001323545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9778861,0.00001153716,0.01483491,0.003834432,0.00003428688,0.0008572021,0.000009699169,0.00005765524,0.002474126],"genre_scores_gemma":[0.9953593,0.000006937245,0.00219536,0.0004760572,0.0000686819,0.0002622422,0.00008205941,0.00001822515,0.001531139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03266965,"threshold_uncertainty_score":0.9998423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09718786473352621,"score_gpt":0.3588957380582129,"score_spread":0.2617078733246867,"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."}}