{"id":"W4401111305","doi":"10.1109/ecti-con60892.2024.10594890","title":"High Performance Deep Learning GPR Feature Detector Model for Potash Mining","year":2024,"lang":"en","type":"article","venue":"","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Potash; Ground-penetrating radar; Computer science; Feature (linguistics); Deep learning; Artificial intelligence; Detector; Feature extraction; Mining engineering; Geology; Materials science; Metallurgy; Radar; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003179932,0.0006285324,0.0005065005,0.0003644776,0.0001814847,0.0004891637,0.001170847,0.0007607721,0.002071986],"category_scores_gemma":[0.0006219412,0.0002558559,0.0005430878,0.0003647644,0.0002021424,0.0005276891,0.0005165637,0.001023278,0.0007155382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006894532,"about_ca_system_score_gemma":0.0007308327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009682599,"about_ca_topic_score_gemma":0.009081746,"domain_scores_codex":[0.9999031,0.000009619673,0.000005217447,0.00003037336,0.00002589279,0.00002584841],"domain_scores_gemma":[0.9998736,0.00003688037,0.00001324376,0.000009912029,0.00005715691,0.000009142569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002001303,0.0001650079,0.003047689,0.00007330917,0.00008999819,0.0001587995,0.00002628397,0.793734,0.009916007,0.002050201,0.003687121,0.1868515],"study_design_scores_gemma":[0.000002267252,0.00001236352,0.0001772184,0.00000214161,0.000004102183,0.000009571176,0.000001616779,0.9986892,0.0007051147,0.0002174908,0.000176643,0.000002240861],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1681177,0.001331718,0.8199284,0.0008080552,0.0002110739,0.00009133571,0.00078138,0.002734265,0.005996141],"genre_scores_gemma":[0.9030256,0.0005113251,0.08600589,0.0002772356,0.00004952476,0.0001420176,0.001297842,0.00006042792,0.008630196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009682599,"threshold_uncertainty_score":0.01925248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01234422676637119,"score_gpt":0.2428174007407547,"score_spread":0.2304731739743835,"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."}}