{"id":"W4389357892","doi":"10.1130/abs/2023am-395190","title":"DETERMINING AQUIFER VULNERABILITY AND RECHARGE RATES IN ALBERTA, CANADA USING HYDROGRAPH DATA ANALYSIS","year":2023,"lang":"en","type":"article","venue":"Abstracts with programs - Geological Society of America","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; University of Waterloo","funders":"","keywords":"Groundwater recharge; Hydrograph; Aquifer; Vulnerability (computing); Hydrology (agriculture); Environmental science; Water resource management; Vulnerability assessment; Geology; Groundwater; Computer science; Geography; Geotechnical engineering; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005729644,0.000288016,0.0002262567,0.00236608,0.001546979,0.001059279,0.000862637,0.000360317,0.001189355],"category_scores_gemma":[0.00163443,0.0002510908,0.0003153978,0.004137458,0.0005818817,0.0003542521,0.0005312301,0.0003088297,0.0001728084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02368418,"about_ca_system_score_gemma":0.0233747,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9979084,"about_ca_topic_score_gemma":0.9990657,"domain_scores_codex":[0.9996525,0.00002773912,0.00002284177,0.00004665336,0.0001511653,0.0000990404],"domain_scores_gemma":[0.998521,0.0002044756,0.0001601022,0.00003347581,0.0008137477,0.0002672736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000126149,0.00005574046,0.9734792,0.00004095669,0.00008627227,0.0002519167,0.0006373294,0.005160246,0.0004524547,0.000334013,0.002436551,0.01693931],"study_design_scores_gemma":[0.00001348868,0.0000103873,0.9877064,0.00002392648,0.00003937098,0.0000495061,0.001943954,0.008451136,0.0002495171,0.000114918,0.001379435,0.00001793286],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922733,0.0002244955,0.0003653357,0.0001823326,0.000005870986,0.00003245975,0.004265116,0.0000501795,0.002600747],"genre_scores_gemma":[0.9947727,0.0002440811,0.000662468,0.00003626454,0.000002496104,0.00001205531,0.002135671,0.000007725659,0.002126532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02368418,"threshold_uncertainty_score":0.1718416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04884322825657938,"score_gpt":0.2869288872863927,"score_spread":0.2380856590298133,"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."}}