{"id":"W4408422955","doi":"10.5194/egusphere-egu25-2836","title":"PEGSGraph: A Graph Neural Network for Fast Earthquake Characterization Based on Prompt ElastoGravity Signals","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Characterization (materials science); Seismology; Graph; Artificial neural network; Computer science; Geology; Artificial intelligence; Theoretical computer science; Materials science; Nanotechnology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005584625,0.0005129076,0.0006282685,0.0003680484,0.0005086335,0.0002134785,0.001027236,0.0004093569,0.00001820542],"category_scores_gemma":[0.00006640713,0.0004642495,0.0004760334,0.0006238055,0.0001220103,0.000137154,0.0006519586,0.0005740081,0.00000998082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001825076,"about_ca_system_score_gemma":0.0001864387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003323051,"about_ca_topic_score_gemma":0.00005595679,"domain_scores_codex":[0.9971901,0.0002542525,0.0004255778,0.00118466,0.000292481,0.0006528925],"domain_scores_gemma":[0.9979081,0.0004384147,0.0002951905,0.0009957466,0.0002614447,0.000101158],"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":[0.0008406989,0.001086289,0.02315259,0.001299582,0.001023857,0.00004309084,0.001301507,0.4026023,0.0002351629,0.05338407,0.02186169,0.4931691],"study_design_scores_gemma":[0.001260783,0.0008669443,0.223987,0.0006098817,0.0001251337,0.000004354842,0.00001220479,0.7303401,0.0003063835,0.02368984,0.01748397,0.001313364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0256725,0.00008720428,0.9637648,0.004065899,0.00292517,0.001671703,0.0001521935,0.0005445508,0.001116019],"genre_scores_gemma":[0.9356641,0.00005681516,0.04894331,0.009783865,0.000650029,0.00123544,0.0007249147,0.00002985428,0.002911637],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9148214,"threshold_uncertainty_score":0.9997809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0210294453008796,"score_gpt":0.254426559041515,"score_spread":0.2333971137406354,"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."}}