{"id":"W3092350050","doi":"10.1002/ieam.4355","title":"Dynamic Bayesian Networks to Assess Anthropogenic and Climatic Drivers of Saltwater Intrusion: A Decision Support Tool Toward Improved Management","year":2020,"lang":"en","type":"article","venue":"Integrated Environmental Assessment and Management","topic":"Groundwater and Isotope Geochemistry","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Saltwater intrusion; Aquifer; Climate change; Environmental science; Bayesian network; Environmental resource management; Water resource management; Computer science; Oceanography; Groundwater; Engineering; Geology; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001754887,0.0002947105,0.0003004237,0.00008123608,0.000117599,0.0001058085,0.0002214725,0.00006790237,0.001718354],"category_scores_gemma":[0.00000117715,0.0002362038,0.00006146895,0.0001456942,0.0001197208,0.0001937688,0.000248736,0.0001434892,0.00002744833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004076145,"about_ca_system_score_gemma":0.000006616182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005754073,"about_ca_topic_score_gemma":0.00003495501,"domain_scores_codex":[0.9983258,0.000039819,0.0004174477,0.0005517594,0.0003096453,0.0003555815],"domain_scores_gemma":[0.9994411,0.00002326801,0.00009094553,0.000200746,0.000005687235,0.0002382382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004221369,0.0001976929,0.2680182,0.0006408186,0.0005559661,0.0002327384,0.0007894892,0.002084752,0.002574297,0.00008878024,0.0006597747,0.7237353],"study_design_scores_gemma":[0.001983858,0.001084069,0.7656111,0.0001504335,0.0003116159,0.00001892899,0.006391046,0.2183801,0.0004289641,0.0002233094,0.004679239,0.0007374089],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9108343,0.0001047009,0.08562978,0.0005128281,0.0001857874,0.001073359,0.00007421282,0.0000465999,0.001538483],"genre_scores_gemma":[0.9758775,0.001441244,0.02161504,0.000551083,0.00001375222,0.00001463426,0.0003382722,0.00001025157,0.0001381729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7229979,"threshold_uncertainty_score":0.9991942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008669855844340034,"score_gpt":0.2223277437178765,"score_spread":0.2136578878735365,"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."}}