{"id":"W4393041576","doi":"10.1007/s11053-024-10331-7","title":"Modified Barnacles Mating Optimizing Algorithm for the Inversion of Self-potential Anomalies Due to Ore Deposits","year":2024,"lang":"en","type":"article","venue":"Natural Resources Research","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bitlis Eren Üniversitesi","keywords":"Inversion (geology); Algorithm; Benchmark (surveying); Computer science; Sensitivity (control systems); Modal; Mathematical optimization; Geology; Mathematics; Engineering; Seismology; Geodesy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0009894393,0.0006636723,0.0007657405,0.000661167,0.0002929324,0.0004536767,0.0009398015,0.0008173902,0.001525244],"category_scores_gemma":[0.001528051,0.0002996241,0.0006057174,0.0004563674,0.0003722597,0.0004321527,0.0006846322,0.0005889175,0.0001751261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003618383,"about_ca_system_score_gemma":0.00058437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002656676,"about_ca_topic_score_gemma":0.001946661,"domain_scores_codex":[0.9997469,0.0001005973,0.00001334068,0.00005002627,0.00005851392,0.00003063301],"domain_scores_gemma":[0.9995729,0.0002411576,0.00004952493,0.00002674917,0.00008719509,0.00002241393],"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":[0.00007316396,0.00004405782,0.001396138,0.00004425236,0.00006866363,0.00008294612,0.00006031892,0.909525,0.003735188,0.003943889,0.0008555002,0.08017094],"study_design_scores_gemma":[0.000005364576,0.00001703386,0.0001114474,0.000001778926,0.000003088966,0.000007418026,0.000003930096,0.9991129,0.0002820798,0.000332241,0.0001204903,0.000002123463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08787546,0.0002744794,0.9093473,0.0001551378,0.00004105203,0.00005006375,0.00003769198,0.0004247147,0.001794081],"genre_scores_gemma":[0.6059081,0.000100565,0.3911408,0.0001435972,0.00003369855,0.0001927407,0.0001419615,0.0001207679,0.002217642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002656676,"threshold_uncertainty_score":0.005282462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03553382867272217,"score_gpt":0.3173644838203714,"score_spread":0.2818306551476492,"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."}}