{"id":"W9683377","doi":"10.1016/s0277-9536(98)00008-2","title":"Observing electromagnetic signals as earthquake precursors in terms of complexity","year":2010,"lang":"en","type":"article","venue":"EGU General Assembly Conference Abstracts","topic":"Earthquake Detection and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Earthquake prediction; Geology; Computer science; Seismology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004579714,0.000225685,0.0003670098,0.000215652,0.0001006638,0.0001190327,0.0003735072,0.000152714,0.006973328],"category_scores_gemma":[0.0001335289,0.0002034573,0.000123003,0.0004362017,0.0001322994,0.0003003781,0.00001607329,0.0004841087,0.0003171743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003067046,"about_ca_system_score_gemma":0.00010815,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006547974,"about_ca_topic_score_gemma":0.08558178,"domain_scores_codex":[0.9980586,0.0001118468,0.0005605908,0.0003962519,0.0003887549,0.000483961],"domain_scores_gemma":[0.9990268,0.0001225774,0.0002466898,0.0003039087,0.00008555767,0.0002144692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008574226,0.0001656187,0.4190392,0.00005115632,0.00005850632,0.00006595456,0.000422997,0.006843203,0.4560172,0.001313844,0.0001016596,0.1158349],"study_design_scores_gemma":[0.0002585513,0.0002278816,0.9608351,0.00002428863,0.00001432158,0.00001633579,0.00004362213,0.006770639,0.02709714,0.004061854,0.000404304,0.0002459666],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743577,0.0000266473,0.000009584634,0.0003538321,0.0002237147,0.0001484872,0.00002153521,0.0000429046,0.02481562],"genre_scores_gemma":[0.9974389,0.00002692393,0.001338882,0.0001507063,0.0001050771,0.000002033702,0.00006436685,0.00000567724,0.0008674121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5417958,"threshold_uncertainty_score":0.9939345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02772641516581972,"score_gpt":0.2476916751485866,"score_spread":0.2199652599827668,"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."}}