{"id":"W2181840190","doi":"","title":"Prestack depth migration of bistatic georadar data","year":2012,"lang":"en","type":"article","venue":"EGU General Assembly Conference Abstracts","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Geology; Prestack; Image (mathematics); Computer science; Algorithm; Seismology; Artificial intelligence","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":[0.0004869437,0.0001446771,0.0001745676,0.00008031274,0.00007533899,0.00006178395,0.0004939883,0.00006791522,0.0004650671],"category_scores_gemma":[0.0000811303,0.0001207988,0.00003088651,0.00012345,0.00007357747,0.001054992,0.00003357092,0.0001272063,0.0001748265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004116312,"about_ca_system_score_gemma":0.0001109323,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01285518,"about_ca_topic_score_gemma":0.0007730493,"domain_scores_codex":[0.9987198,0.00006907211,0.0003219264,0.0002360798,0.0003001595,0.0003530298],"domain_scores_gemma":[0.9989975,0.00007917176,0.0001964285,0.0004915182,0.00007890332,0.0001564937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006532444,0.0002041735,0.5066243,0.000126824,0.00006811013,0.00001141462,0.001005935,0.0009978662,0.01371366,0.0006777243,0.1498105,0.3266941],"study_design_scores_gemma":[0.0001863915,0.0001029743,0.8590268,0.00004862349,0.00003436199,0.00001537317,0.0002131188,0.03856486,0.04317074,0.0005224884,0.05779979,0.000314434],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851305,0.000307827,0.0008239998,0.0005822332,0.0003073673,0.0001265663,0.0001163597,0.00008203728,0.01252314],"genre_scores_gemma":[0.9891208,0.00009799511,0.008871747,0.0004880845,0.0001598492,6.898125e-7,0.000788153,0.000004193626,0.0004684207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3524025,"threshold_uncertainty_score":0.9937183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07288523678259515,"score_gpt":0.2810591370348006,"score_spread":0.2081739002522055,"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."}}