{"id":"W1491868200","doi":"","title":"On the estimation of stochastic parameters from deep seismic reflection data and its use in delineating lower crustal structure","year":2007,"lang":"en","type":"dissertation","venue":"Data Archiving and Networked Services (DANS)","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geology; Seismology; Reflection (computer programming); Synthetic seismogram; Seismic to simulation; Deformation (meteorology); Synthetic data; Seismic inversion; Statistics; Mathematics; Azimuth; Geometry; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005757571,0.0002679461,0.0002783985,0.0001757071,0.0002132573,0.0001497295,0.000815879,0.0001410491,0.00002734988],"category_scores_gemma":[0.0001280505,0.0001931124,0.00001741155,0.0002035872,0.00004939632,0.0006034689,0.000106625,0.0004909832,0.000001664207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005556109,"about_ca_system_score_gemma":0.00002391259,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02281518,"about_ca_topic_score_gemma":0.01903625,"domain_scores_codex":[0.9982977,0.0001781951,0.0003789807,0.000633345,0.0002668307,0.0002449355],"domain_scores_gemma":[0.9975719,0.001301015,0.0002999299,0.0007411878,0.0000241403,0.00006179051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001745102,0.00003940736,0.01446383,0.0006797413,0.0001740554,0.000021108,0.007088142,0.3600458,0.0002401651,0.00001525892,0.0008195438,0.6146678],"study_design_scores_gemma":[0.0001377747,0.000071228,0.01460925,0.0008592866,0.00007264771,0.000004476411,0.0008551146,0.9825958,0.00007276652,0.0004865306,0.00004365555,0.0001914941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921433,0.0005047856,0.004405135,0.00002457101,0.0003103405,0.0002451847,0.002259835,0.00004262654,0.00006421591],"genre_scores_gemma":[0.9420193,0.0001386483,0.002278881,0.0003068515,0.00006697957,7.656549e-7,0.05515807,0.00001186917,0.00001861405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.62255,"threshold_uncertainty_score":0.9988638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03000790899403831,"score_gpt":0.2757199283238006,"score_spread":0.2457120193297623,"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."}}