{"id":"W4412670692","doi":"10.1038/s41598-025-10138-3","title":"Reliable and efficient magnetic data inversion for resource detection using a hybrid bat algorithm","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Cairo University","keywords":"Algorithm; Robustness (evolution); Inversion (geology); Computer science; Petrophysics; Synthetic data; Gaussian; Noise (video); Sensor fusion; Geophysics; Geology; Artificial intelligence; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001419699,0.000102163,0.0001457677,0.0001515509,0.000559016,0.0002212103,0.0001590222,0.00003916079,0.00006330783],"category_scores_gemma":[0.0002610515,0.00008268624,0.00003654186,0.0006863812,0.0001412295,0.0001096742,0.00007461217,0.00008818823,0.000007417243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000673405,"about_ca_system_score_gemma":0.00007886095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005974668,"about_ca_topic_score_gemma":0.00005006368,"domain_scores_codex":[0.9983554,0.00004738873,0.0002543147,0.0007914262,0.0002504667,0.0003009771],"domain_scores_gemma":[0.9990028,0.0001323136,0.00009076122,0.0006017841,0.00006346886,0.0001089146],"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":[0.00002271786,0.0000277628,0.002086704,0.00004628297,0.000006305926,0.00003128525,0.00001699607,0.001929906,0.001763187,0.000002697146,0.003922205,0.990144],"study_design_scores_gemma":[0.0001199779,0.00008084646,0.006056553,0.00002854136,0.00004007204,0.00005091023,0.00002215462,0.8682971,0.006082845,0.00688338,0.112214,0.0001236408],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9485828,0.001243283,0.04361711,0.0001485388,0.004709175,0.0005977991,0.00004042152,0.00006491833,0.000995945],"genre_scores_gemma":[0.9492837,0.000005284952,0.04551781,0.0001337933,0.0001217903,0.000003030862,0.0003131543,0.000004891352,0.004616562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9900203,"threshold_uncertainty_score":0.4299556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02101109055884725,"score_gpt":0.2507796883849437,"score_spread":0.2297685978260965,"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."}}