{"id":"W4401875979","doi":"10.1190/gem2024-036.1","title":"Background EM noise characterization in deep underground mining tunnels","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":"Université du Québec","funders":"","keywords":"Noise (video); Characterization (materials science); Computer science; Mining engineering; Geology; Artificial intelligence; Materials science","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":[0.0004915801,0.0004028048,0.0003131939,0.001043575,0.0003116009,0.0005749744,0.0004474724,0.0005454011,0.001189203],"category_scores_gemma":[0.001136958,0.0001371082,0.0001916956,0.0007637095,0.0004098563,0.0006306247,0.0005702002,0.0002773077,0.0006127598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001853174,"about_ca_system_score_gemma":0.0002558712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009216369,"about_ca_topic_score_gemma":0.001014547,"domain_scores_codex":[0.999544,0.0000620561,0.00002510365,0.00009915666,0.0002137723,0.00005587827],"domain_scores_gemma":[0.9992022,0.0001683961,0.0001371304,0.00006850773,0.0003356214,0.00008818549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001070945,0.0002920029,0.1085124,0.001492975,0.00007794317,0.002105472,0.00125152,0.08265975,0.5006269,0.003477434,0.004780797,0.2936519],"study_design_scores_gemma":[0.00009683093,0.001865478,0.2801616,0.0008239622,0.0001950383,0.01043639,0.002771527,0.3529303,0.2668343,0.01161096,0.07190719,0.0003662208],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7185055,0.001954502,0.2695329,0.0002275946,0.0001232489,0.0001155339,0.001054198,0.0009867618,0.007499682],"genre_scores_gemma":[0.9586177,0.0007861469,0.03586751,0.0001141402,0.00007285368,0.00007417696,0.001323866,0.0000969211,0.003046627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001189203,"threshold_uncertainty_score":0.003978252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02117930285765049,"score_gpt":0.266181091918531,"score_spread":0.2450017890608805,"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."}}