{"id":"W2075699133","doi":"10.3997/2214-4609.20140683","title":"High Fidelity Imaging with Least Squares Migration","year":2014,"lang":"en","type":"article","venue":"Proceedings","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shell Canada","keywords":"Fidelity; Computer science; High fidelity; Least-squares function approximation; Multiple; Algorithm; Data science; Artificial intelligence; Telecommunications; Statistics; Mathematics; Engineering; Arithmetic; Electrical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002449318,0.00009204429,0.00008811102,0.00006241655,0.0001352767,0.0001060446,0.0001178016,0.00002077155,0.0002616256],"category_scores_gemma":[0.00004252428,0.00006723413,0.00001899901,0.000125708,0.00006487351,0.0004108308,0.000005484789,0.00008890286,0.00008928351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003526605,"about_ca_system_score_gemma":0.000009925739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005588966,"about_ca_topic_score_gemma":0.00004314034,"domain_scores_codex":[0.9993414,0.00000604897,0.0001026657,0.0001974279,0.0001764282,0.0001760386],"domain_scores_gemma":[0.999718,0.00001912766,0.00005915316,0.00005375705,0.00009030724,0.00005964426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003214879,0.000006936209,0.7772152,0.00002858663,0.000003730971,6.118154e-7,0.000265779,0.00002139356,0.0002469655,0.0004746067,0.03602569,0.1856783],"study_design_scores_gemma":[0.0006170966,0.0003741759,0.4417181,0.0001649389,0.00003661138,0.00007270066,0.001070391,0.2641027,0.02250097,0.01068377,0.2579947,0.0006639106],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.969423,0.00007108577,0.002167151,0.004023753,0.0001084779,0.0001039435,0.000005751298,0.0004094213,0.02368743],"genre_scores_gemma":[0.9927628,0.000008826006,0.005034435,0.001910829,0.0001090407,0.000001112031,0.00002017343,0.000003108529,0.0001496936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3354971,"threshold_uncertainty_score":0.8448883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006395035833691149,"score_gpt":0.1861008839862729,"score_spread":0.1797058481525817,"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."}}