{"id":"W4220940877","doi":"10.5194/egusphere-egu22-10686","title":"From datasets to decisions &amp;#8211; a repeatable workflow for groundwater decision support","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Intertek (Canada)","funders":"","keywords":"Workflow; Data assimilation; Computer science; Groundwater recharge; Probabilistic logic; Decision support system; Data mining; Hydrogeology; Groundwater model; Data science; Risk analysis (engineering); Groundwater; Database; Artificial intelligence; Engineering","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009300826,0.0004912405,0.0006306378,0.0003965485,0.0001483903,0.0002891798,0.0009398547,0.0003220951,0.005699387],"category_scores_gemma":[0.0007363394,0.0004806278,0.0002748821,0.0002836421,0.000009034066,0.0001457606,0.001283357,0.0006687336,0.0001887156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002558531,"about_ca_system_score_gemma":0.00005492568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001903628,"about_ca_topic_score_gemma":0.00007227754,"domain_scores_codex":[0.997268,0.00006049966,0.0007571736,0.000845599,0.0005296355,0.0005391195],"domain_scores_gemma":[0.9956596,0.001680373,0.00004662668,0.00220347,0.00007050288,0.0003393946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004842707,0.00002280579,0.00002971333,0.00003548023,0.00007781204,0.000005535705,0.0001348349,0.8220255,0.0001163206,0.00002072123,0.1642009,0.01328192],"study_design_scores_gemma":[0.0004637787,0.00003209032,0.0001480248,0.0001098239,0.00004956485,0.00000205504,0.00002328002,0.2199382,0.0001512164,0.008009307,0.7704645,0.0006081131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04953066,0.0001871918,0.9398872,0.00008030249,0.003404723,0.0009007553,0.004057667,0.0008068868,0.001144632],"genre_scores_gemma":[0.007183164,0.0001048761,0.9700938,0.0001102435,0.0005033534,0.0007672739,0.01756518,0.0001928311,0.003479225],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6062636,"threshold_uncertainty_score":0.9997646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05976501722599668,"score_gpt":0.3467088659125696,"score_spread":0.2869438486865729,"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."}}