{"id":"W4382559830","doi":"10.18280/mmep.100313","title":"Supervised Classification of Groundwater Potential Mapping Using Integrated Machine Learning and GIS-Based Techniques","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Groundwater and Watershed Analysis","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Water Resources","keywords":"Computer science; Groundwater; Artificial intelligence; Machine learning; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003493746,0.0001417916,0.0002044045,0.0001280143,0.00008187805,0.00005405776,0.00006181556,0.00006765859,0.00003111926],"category_scores_gemma":[0.00001005378,0.0001138668,0.00004146505,0.0002662534,0.00006190165,0.0000982483,0.00006078073,0.0001321398,0.000008826063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002749049,"about_ca_system_score_gemma":0.000002023672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001389081,"about_ca_topic_score_gemma":9.391934e-7,"domain_scores_codex":[0.9991264,0.00002190368,0.0002680404,0.0002182302,0.0001524364,0.0002129978],"domain_scores_gemma":[0.999772,0.00002909021,0.00003807716,0.00008752693,0.000009820863,0.0000634571],"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":[0.000004086909,0.00002849648,0.001627412,0.0003459815,0.00002007576,0.000001877691,0.0006981233,0.8652893,0.1304894,0.00007904942,0.000001151699,0.001415069],"study_design_scores_gemma":[0.0001000122,0.00002598471,0.0001218037,0.0001323473,0.00002860685,0.000004640156,0.00005911666,0.9959556,0.002022166,0.001353253,0.00006075887,0.0001356855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4518348,0.00001205262,0.5478997,0.0000376546,0.000006581798,0.0000655394,7.265864e-7,0.0001145853,0.00002838058],"genre_scores_gemma":[0.9548594,0.00002104677,0.04498541,0.000003366901,0.000007410136,0.00001142691,0.00002096406,0.00002177262,0.00006920162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5030246,"threshold_uncertainty_score":0.4643354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02822176837260095,"score_gpt":0.2140924509831764,"score_spread":0.1858706826105754,"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."}}