{"id":"W4408444098","doi":"10.5194/egusphere-egu25-2328","title":"An extension of the diffraction hyperbola method to layered media","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"ASTER","funders":"","keywords":"Hyperbola; Extension (predicate logic); Diffraction; Mathematics; Computer science; Geometry; Physics; Optics; Programming language","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":[0.0002005206,0.0001551541,0.0002905664,0.00008261731,0.00005463543,0.00002985906,0.0003344664,0.00006828209,0.0003492374],"category_scores_gemma":[0.000009051427,0.0001043563,0.0002197534,0.0001814905,0.00001144769,0.00001801334,0.000277211,0.0002653613,0.00000531883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001386887,"about_ca_system_score_gemma":0.00006742372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002269698,"about_ca_topic_score_gemma":0.00002448357,"domain_scores_codex":[0.9989827,0.0001508461,0.0002263665,0.0003455163,0.0001604081,0.000134216],"domain_scores_gemma":[0.9988977,0.00009544078,0.0001185825,0.0007414018,0.00009316218,0.00005369147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003896488,0.0005491855,0.01443476,0.00009683918,0.000539455,3.847676e-7,0.0009649311,0.01761799,0.4497162,0.006279383,0.002980799,0.5067812],"study_design_scores_gemma":[0.001014509,0.0002855592,0.3346163,0.001090927,0.00211839,0.000001583491,0.002067983,0.07877709,0.5134835,0.05989512,0.00492476,0.001724306],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7100834,0.00003625663,0.2739784,0.001497023,0.0005063297,0.0002833441,0.00006827879,0.00004871721,0.01349834],"genre_scores_gemma":[0.9776742,0.000001584243,0.01956719,0.0001222,0.0002031473,0.00002408106,0.00003397396,0.000008371748,0.002365224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5050569,"threshold_uncertainty_score":0.4255526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01229684214110152,"score_gpt":0.3050113649776885,"score_spread":0.292714522836587,"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."}}