{"id":"W2155672135","doi":"10.1190/1.3377009","title":"Unexploded ordnance discrimination using magnetic and electromagnetic sensors: Case study from a former military site","year":2010,"lang":"en","type":"article","venue":"Geophysics","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital; Quest University Canada; University of British Columbia","funders":"","keywords":"Magnetometer; EMI; Unexploded ordnance; Electromagnetic interference; Acoustics; Computer science; Geology; Remote sensing; Pattern recognition (psychology); Artificial intelligence; Physics; Telecommunications; Magnetic field","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004886762,0.0001741568,0.0001728349,0.00003015987,0.0001136939,0.00002655716,0.0000641054,0.00004880064,0.00001293179],"category_scores_gemma":[0.0000176238,0.0001745768,0.00003607575,0.000206397,0.00004373522,0.000141035,0.00003358744,0.0002442543,0.00001979605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001153663,"about_ca_system_score_gemma":0.000007211811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001504719,"about_ca_topic_score_gemma":0.0004601712,"domain_scores_codex":[0.9992175,0.00002809242,0.0001658675,0.0002517445,0.0001050868,0.0002317049],"domain_scores_gemma":[0.99942,0.0001158572,0.00002049759,0.0003233811,0.0000429577,0.00007727334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005284166,0.0002212672,0.0003737093,0.00004039265,0.00002405245,0.00007762029,0.00262731,0.0003432817,0.9497097,0.0002563477,0.00002062402,0.04630041],"study_design_scores_gemma":[0.002435302,0.001065663,0.1945958,0.00005883408,0.0007607108,0.0003184209,0.004981734,0.7388101,0.008261748,0.04633183,0.000594879,0.00178507],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977794,0.0001388667,0.001391851,0.00003007503,0.000106368,0.0003624462,0.00003038695,0.0001151485,0.00004547893],"genre_scores_gemma":[0.9822098,0.00001201479,0.01746328,0.00001843836,0.0001727703,0.00005553157,0.00001166708,0.00003317421,0.00002329538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.941448,"threshold_uncertainty_score":0.7119036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01284643260899645,"score_gpt":0.2481917148547424,"score_spread":0.235345282245746,"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."}}