{"id":"W3003574882","doi":"10.1016/j.dib.2020.105209","title":"Validation data of parallel 3D surface-borehole electromagnetic forward modeling","year":2020,"lang":"en","type":"article","venue":"Data in Brief","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"Fonds de recherche du Québec – Nature et technologies; Commonwealth Scientific and Industrial Research Organisation","keywords":"Polygon mesh; Computer science; Discretization; Computational science; Computational electromagnetics; Supercomputer; Parallel computing; Domain (mathematical analysis); 3D modeling; Algorithm; Electromagnetic field; Computer graphics (images); Physics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007752407,0.0008207819,0.0004443935,0.0005681154,0.0005038148,0.0006446015,0.0009394411,0.0007777321,0.003864733],"category_scores_gemma":[0.001836486,0.0002073176,0.0006514026,0.0006037183,0.0005113108,0.0006478077,0.0006292163,0.0007828694,0.001101851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003492982,"about_ca_system_score_gemma":0.000933233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008275111,"about_ca_topic_score_gemma":0.004650961,"domain_scores_codex":[0.9995595,0.00006734522,0.00002775681,0.00007646489,0.0002175569,0.00005141052],"domain_scores_gemma":[0.9988017,0.0002806088,0.00005075916,0.0002908912,0.0005202707,0.00005578994],"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":[0.0003909481,0.0003335013,0.01202965,0.000198185,0.0000551041,0.0004024937,0.0001563083,0.907212,0.0247721,0.004454407,0.006996978,0.04299843],"study_design_scores_gemma":[0.00005152929,0.0001203612,0.003251589,0.00001478952,0.00001103771,0.00008346881,0.00006139737,0.9709425,0.02036266,0.001106158,0.003969918,0.00002458999],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.8100973,0.0002612687,0.1515486,0.000505748,0.0002977464,0.0002422204,0.008882384,0.005464346,0.02270043],"genre_scores_gemma":[0.9597496,0.0001042341,0.03183547,0.0000517744,0.0000162806,0.0001317954,0.005667217,0.0002682637,0.002175441],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.008275111,"threshold_uncertainty_score":0.01645392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08540578016670639,"score_gpt":0.2882629902148751,"score_spread":0.2028572100481687,"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."}}