{"id":"W2900338257","doi":"10.1111/gwat.12842","title":"Natural Stimuli Calibration with Fining Direction Regularization in an Integrated Hydrologic Model","year":2018,"lang":"en","type":"article","venue":"Ground Water","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"DuPont","keywords":"Sedimentary depositional environment; Geology; Flood myth; Regularization (linguistics); Calibration; Groundwater; Inversion (geology); Hydrology (agriculture); Computer science; Geotechnical engineering; Geomorphology; Artificial intelligence; Mathematics; 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":[0.0001361223,0.0001097403,0.00008791984,0.00004735247,0.0001694025,0.00007192825,0.00007414427,0.00005080307,0.0001376108],"category_scores_gemma":[0.000004001072,0.00006616378,0.00001147352,0.0001445075,0.0001265297,0.0008750221,0.00004922421,0.00007810927,0.00006362564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001160663,"about_ca_system_score_gemma":0.000003300769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002569916,"about_ca_topic_score_gemma":0.001479045,"domain_scores_codex":[0.9992124,0.00004794378,0.0001338006,0.0002589288,0.000153927,0.0001930256],"domain_scores_gemma":[0.9998109,0.000005615981,0.0000264128,0.0001115815,0.00001954279,0.00002592945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001081698,0.0005648651,0.4770807,0.00002126032,0.00006559306,0.00003246399,0.03905607,0.1078031,0.2686469,0.0006838331,0.00063464,0.1043289],"study_design_scores_gemma":[0.0006027853,0.0003346046,0.0678402,0.00001078013,0.00001267801,0.000006405042,0.0002183157,0.9150054,0.01398826,0.0008568134,0.0008684409,0.0002553114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.90373,0.000002803137,0.09521653,0.0001597102,0.00008596054,0.0001075026,6.817027e-7,0.00005731103,0.0006394601],"genre_scores_gemma":[0.9934498,6.101541e-7,0.001073751,0.0002088772,0.00003492611,0.00002340578,0.00008326252,0.000009388556,0.005115949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8072023,"threshold_uncertainty_score":0.2698081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01519337737483172,"score_gpt":0.2229750216551377,"score_spread":0.207781644280306,"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."}}