{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00128616,0.0005889264,0.0006427186,0.003014044,0.0002086287,0.0008222982,0.0006222044,0.0004603103,0.0005894293],"category_scores_gemma":[0.003438762,0.0002055605,0.0005364804,0.001738906,0.0002534314,0.001073695,0.0005506305,0.0002674234,0.0002281414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003667367,"about_ca_system_score_gemma":0.0006028242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001495038,"about_ca_topic_score_gemma":0.00274129,"domain_scores_codex":[0.9993761,0.0002543278,0.0000454832,0.00009118454,0.0002014254,0.00003143168],"domain_scores_gemma":[0.9984313,0.0007205012,0.000266605,0.0001498986,0.000402192,0.00002957321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001158754,0.0003129635,0.04442499,0.0002846637,0.0002303339,0.0001878443,0.0002050561,0.3911791,0.01402298,0.004637392,0.001361808,0.543037],"study_design_scores_gemma":[0.000005939534,0.0000681768,0.006107789,0.00001782323,0.00002314607,0.00006124767,0.00009216881,0.9840549,0.004937861,0.003774648,0.0008417363,0.00001470062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.200144,0.0004475504,0.7944546,0.0001969574,0.00003813612,0.0001137865,0.0004152735,0.00108205,0.003107709],"genre_scores_gemma":[0.7953181,0.0002141883,0.2032887,0.00003847784,0.00002770387,0.00009057722,0.0004299362,0.00002745983,0.0005648471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003014044,"threshold_uncertainty_score":0.006801963,"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."}}