{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007672113,0.001962591,0.0009241095,0.002669051,0.001590659,0.005101936,0.002954608,0.001333125,0.03071191],"category_scores_gemma":[0.02213139,0.001166838,0.002392342,0.002607462,0.000952311,0.003017532,0.003687369,0.003214493,0.02054034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002777679,"about_ca_system_score_gemma":0.005843777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01223177,"about_ca_topic_score_gemma":0.01583205,"domain_scores_codex":[0.9964162,0.001000889,0.0005382109,0.0009664611,0.0008818543,0.00019637],"domain_scores_gemma":[0.9844897,0.005999034,0.0007156711,0.004861364,0.003044239,0.0008900748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009931609,0.0006565082,0.007536931,0.001278581,0.0002583253,0.001228026,0.001876534,0.07288083,0.01259117,0.05154276,0.5097004,0.3394569],"study_design_scores_gemma":[0.000504792,0.000177368,0.004921725,0.0005318006,0.00006828945,0.0002599352,0.001002329,0.2398018,0.02488296,0.1158221,0.6117262,0.0003007821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01595205,0.0002904185,0.5727823,0.003987842,0.0005188989,0.003253971,0.1695633,0.2057936,0.02785754],"genre_scores_gemma":[0.06492473,0.000298247,0.7360554,0.0009629353,0.000101015,0.003192181,0.1750876,0.01173733,0.007640462],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03071191,"threshold_uncertainty_score":0.1027416,"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."}}