{"id":"W2985735369","doi":"10.1080/22020586.2019.12073229","title":"Sensitivity-based data reduction of large 3D DC/IP surveys","year":2019,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Condor Petroleum (Canada)","funders":"","keywords":"Sensitivity (control systems); Computation; Reduction (mathematics); Computer science; Algorithm; Inversion (geology); Data reduction; Data mining; Synthetic data; Mathematics; Geology; Engineering; Electronic engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00237085,0.000126037,0.000186719,0.0001102484,0.00006446298,0.000027452,0.000236747,0.00007989778,0.00110602],"category_scores_gemma":[0.0001001986,0.0001133271,0.00004114173,0.0001577406,0.00006090558,0.0004354421,0.00002337194,0.0001793527,0.000512877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000418666,"about_ca_system_score_gemma":0.00009441437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006082931,"about_ca_topic_score_gemma":0.000111316,"domain_scores_codex":[0.9985816,0.0002464501,0.000248201,0.0003580357,0.0002906008,0.000275093],"domain_scores_gemma":[0.9987942,0.0001822942,0.0001692378,0.000707897,0.00006336418,0.00008298678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009428539,0.0002640797,0.02288143,0.0001447749,0.00004833041,0.00007219629,0.0000972939,0.0031312,0.003288263,0.0000449023,0.03731199,0.9326212],"study_design_scores_gemma":[0.0004399925,0.0001142753,0.8830482,0.00006308349,0.00001867136,0.00003360935,0.0001284046,0.05476275,0.04377871,0.0002148844,0.01715274,0.0002447235],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878017,0.0001040428,0.0009912606,0.0004790386,0.0007233523,0.0002255085,0.0008139653,0.0001897075,0.008671471],"genre_scores_gemma":[0.9947166,0.00001379432,0.002829792,0.0004501268,0.00006252381,1.926833e-7,0.001655197,0.000005083459,0.0002666702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9323765,"threshold_uncertainty_score":0.9998071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02506136385412204,"score_gpt":0.2564806731229399,"score_spread":0.2314193092688179,"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."}}