{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005541605,0.0000927123,0.00008901369,0.00002870264,0.00006754485,0.00004909091,0.00006721079,0.00005082561,0.00001805183],"category_scores_gemma":[0.00001134383,0.00007987258,0.00004687812,0.0001239264,0.000006991793,0.00007209973,0.00001340142,0.0001524344,0.00003571244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001684495,"about_ca_system_score_gemma":0.000004578351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000141488,"about_ca_topic_score_gemma":0.000001698279,"domain_scores_codex":[0.9995702,0.00000366445,0.00007237388,0.0001326421,0.00005071651,0.0001704133],"domain_scores_gemma":[0.9997574,0.00009300721,0.000005007863,0.00008826836,0.00001521376,0.00004110789],"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":[0.000002463714,0.00000606718,0.00001425102,0.0002737086,0.00003123011,3.768628e-7,0.0002815007,0.6093374,0.04666149,0.005905354,0.001069349,0.3364168],"study_design_scores_gemma":[0.00004869458,0.00001522248,0.0003103582,0.00002102555,0.00001348404,8.098786e-7,0.00002213795,0.9902346,0.003214605,0.000637269,0.005369246,0.0001125425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.410287,0.0002017193,0.587633,0.0001080821,0.0001111602,0.000109947,0.000004159818,0.000592773,0.0009521643],"genre_scores_gemma":[0.76585,0.00001784933,0.231226,0.00001629302,0.0001214222,0.000102251,0.000005186061,0.00002686792,0.002634133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3808973,"threshold_uncertainty_score":0.325711,"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."}}