{"id":"W2976772718","doi":"10.3390/w11102013","title":"A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping","year":2019,"lang":"en","type":"article","venue":"Water","topic":"Groundwater and Watershed Analysis","field":"Environmental Science","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Universiti Teknologi Malaysia; Korea Institute of Geoscience and Mineral Resources","keywords":"Support vector machine; Logistic model tree; Random forest; Decision tree; Receiver operating characteristic; AdaBoost; Mean squared error; Computer science; Artificial intelligence; Benchmark (surveying); Logistic regression; Machine learning; Classifier (UML); Data mining; Statistics; Mathematics; Cartography; Geography","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002095209,0.0001629735,0.0001609497,0.00008208211,0.00009959601,0.0001407408,0.000294813,0.00003025208,0.002289859],"category_scores_gemma":[0.000001155309,0.0001100843,0.0001000502,0.00009631153,0.00004610549,0.0002990582,0.0003799296,0.00009494838,0.01662971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001005247,"about_ca_system_score_gemma":0.000001867102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006072146,"about_ca_topic_score_gemma":0.000003553208,"domain_scores_codex":[0.9984919,0.00003514516,0.0002253257,0.0004647902,0.0003427309,0.0004400531],"domain_scores_gemma":[0.9996132,0.000005216181,0.00001911385,0.0002346701,0.000009024685,0.000118839],"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":[0.00004457374,0.0002998311,0.1025364,0.0000442331,0.0001099487,0.00003167234,0.004839748,0.835789,0.04890974,0.0002304715,0.0007056136,0.006458743],"study_design_scores_gemma":[0.001528535,0.0004100247,0.1825278,0.00008685642,0.0001797251,0.0003250124,0.001386674,0.5225463,0.1733469,0.02867483,0.08521771,0.003769611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7489086,0.000001293285,0.244893,0.0002524153,0.0001171483,0.0001574729,8.788124e-7,0.00003932995,0.005629824],"genre_scores_gemma":[0.9865803,3.300412e-7,0.009278849,0.0006153287,0.00004897376,0.00001401861,0.00002969415,0.00001821059,0.003414326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3132427,"threshold_uncertainty_score":0.9986222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009416910060683853,"score_gpt":0.1908208591357239,"score_spread":0.18140394907504,"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."}}