{"id":"W4405867268","doi":"10.1016/j.jes.2024.12.024","title":"Improving groundwater vulnerability assessment using machine learning","year":2024,"lang":"en","type":"article","venue":"Journal of Environmental Sciences","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Health","keywords":"Vulnerability (computing); Groundwater; Vulnerability assessment; Computer science; Environmental science; Water resource management; Artificial intelligence; Geology; Psychology; Computer security; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007860624,0.0005482986,0.0005851327,0.001886321,0.000279237,0.0006305514,0.0005111446,0.0006564157,0.00126334],"category_scores_gemma":[0.003358892,0.0002160229,0.000525399,0.0007467004,0.0002438512,0.001090021,0.0007547516,0.0005067366,0.0002061935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004296112,"about_ca_system_score_gemma":0.0005752268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003723553,"about_ca_topic_score_gemma":0.004031128,"domain_scores_codex":[0.9996967,0.00009533168,0.0000207272,0.0000527005,0.0000926063,0.00004192525],"domain_scores_gemma":[0.9986271,0.0008636682,0.0001039051,0.00008828413,0.0002791407,0.00003788806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007647919,0.0001309511,0.01212614,0.00005056157,0.0001112808,0.00007778542,0.00003899479,0.8086231,0.003825059,0.001483233,0.0010208,0.1724357],"study_design_scores_gemma":[0.000001804327,0.00001289953,0.0006002727,0.000002223443,0.000006385199,0.000007073678,0.000007098655,0.9975693,0.0005918083,0.001110815,0.00008774381,0.000002488248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3865986,0.0004827849,0.6072804,0.0004309099,0.00005800171,0.00005821873,0.0003183254,0.001150126,0.003622604],"genre_scores_gemma":[0.9557347,0.00007656661,0.04330055,0.00003829837,0.00002221536,0.00001665225,0.0001706674,0.00002314855,0.0006170936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003723553,"threshold_uncertainty_score":0.007403791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01955512090769964,"score_gpt":0.2953270202619819,"score_spread":0.2757718993542823,"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."}}