{"id":"W4405631822","doi":"10.1016/j.rines.2024.100051","title":"Spatial characterisation of groundwater systems using fuzzy c-mean clustering: A multi-parameter approach in crystalline aquifers","year":2024,"lang":"en","type":"article","venue":"Results in Earth Sciences","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Energy","funders":"","keywords":"Aquifer; Groundwater; Cluster analysis; Fuzzy logic; Environmental science; Groundwater resources; Water resource management; Hydrology (agriculture); Geology; Data mining; Soil science; Computer science; Geotechnical engineering; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008495043,0.0004240523,0.0005006992,0.004813668,0.0007543194,0.001254588,0.0007947982,0.0007545495,0.0004812059],"category_scores_gemma":[0.00183453,0.0002587482,0.0008015591,0.002860988,0.0004716792,0.0005992102,0.0005078653,0.0002935869,0.00008911567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001103375,"about_ca_system_score_gemma":0.0009940701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.031665,"about_ca_topic_score_gemma":0.01988997,"domain_scores_codex":[0.9995572,0.00009145233,0.00004192219,0.000113733,0.0001313927,0.00006425467],"domain_scores_gemma":[0.9993393,0.0002524318,0.0001063834,0.00004792143,0.0002235534,0.00003035501],"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":[0.000223621,0.0001186402,0.04180609,0.0002291661,0.0001622378,0.0002908333,0.0009810831,0.7779115,0.01634269,0.004984158,0.0008773866,0.1560726],"study_design_scores_gemma":[0.000003448061,0.00002480888,0.01248875,0.00001313654,0.00002008919,0.00005100447,0.0003557095,0.9831606,0.001991595,0.001574353,0.0002843583,0.00003215141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5265643,0.0003365878,0.4702346,0.0001305859,0.00001446158,0.0001868997,0.0004301278,0.0003423611,0.001760235],"genre_scores_gemma":[0.9165428,0.00008302342,0.08263022,0.00001093191,0.00000648681,0.00005803485,0.0002961597,0.00001998033,0.0003523309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.031665,"threshold_uncertainty_score":0.0629614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07866996826638287,"score_gpt":0.3322263417858929,"score_spread":0.2535563735195101,"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."}}