{"id":"W2612422058","doi":"10.3390/hydrology4020028","title":"Understanding the Effects of Parameter Uncertainty on Temporal Dynamics of Groundwater-Surface Water Interaction","year":2017,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Royal University; University of Northern British Columbia","funders":"","keywords":"Environmental science; Uncertainty analysis; Groundwater; Groundwater flow; Watershed; Hydrology (agriculture); Climate change; Monte Carlo method; Greenhouse gas; Flow (mathematics); Surface water; Statistics; Environmental engineering; Geology; Mathematics; Aquifer; Computer science","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":[0.0002656467,0.0001094039,0.0001888438,0.00001998708,0.000308319,0.00000999397,0.0002996635,0.00007457266,0.0001550126],"category_scores_gemma":[0.00004601256,0.00006051772,0.00005636919,0.00001768574,0.0008108027,0.0001094428,0.0003332373,0.0001230231,0.00006156838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009891761,"about_ca_system_score_gemma":9.572274e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007407508,"about_ca_topic_score_gemma":0.0007168972,"domain_scores_codex":[0.9992078,0.0001094819,0.0001663185,0.0001911648,0.000102951,0.000222281],"domain_scores_gemma":[0.9992515,0.0001875107,0.0001511982,0.0003895879,0.000003104399,0.00001707743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00112823,0.0005748353,0.8571202,0.0002175491,0.0006245855,0.00005876389,0.00616538,0.1044845,0.02011402,0.006144489,0.00223364,0.001133801],"study_design_scores_gemma":[0.008735904,0.01474724,0.2175736,0.0002840417,0.001007719,0.00005726755,0.00213468,0.3055685,0.1332129,0.307069,0.007733074,0.001876052],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900866,0.000002545033,0.001838207,0.002602897,0.0004263243,0.000182481,9.336358e-7,0.00001003779,0.00484997],"genre_scores_gemma":[0.9993865,0.00001080494,0.00003105724,0.0002056297,0.00001115488,0.000006714446,0.000005806824,0.00000645445,0.0003358558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6395466,"threshold_uncertainty_score":0.2987437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03050287749871503,"score_gpt":0.2550260190705862,"score_spread":0.2245231415718712,"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."}}