{"id":"W4321492370","doi":"10.5194/egusphere-egu23-1263","title":"Earthquake Early Warning: observe, analyse, deduce and act in seconds","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ocean Networks Canada Society; University of Victoria","funders":"","keywords":"GNSS applications; Warning system; Subduction; Seismology; Architecture; Computer science; Key (lock); Property (philosophy); Latency (audio); Computer security; Warning signs; Geology; Global Positioning System; Geography; Telecommunications; Transport engineering; Engineering; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006494519,0.0003338536,0.0005522201,0.0004129555,0.0001376944,0.0002151521,0.0008925939,0.0003647209,0.00003328796],"category_scores_gemma":[0.0001415708,0.0003087236,0.000122763,0.0005234306,0.0001135677,0.0002363292,0.002814209,0.0009262842,0.0001616425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002271577,"about_ca_system_score_gemma":0.0001396471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001362451,"about_ca_topic_score_gemma":0.001816563,"domain_scores_codex":[0.9976966,0.0001753748,0.0004077075,0.001029154,0.0002017358,0.0004894742],"domain_scores_gemma":[0.9985557,0.0003145346,0.0001382906,0.0008118593,0.00007081062,0.000108815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001453474,0.00006000426,0.9479147,0.0001205303,0.0003396867,0.0002976248,0.006111948,0.001469659,0.00001075894,0.005356549,0.002005576,0.03629846],"study_design_scores_gemma":[0.0002419008,0.00005121091,0.9830747,0.00005573598,0.00001484564,0.000009571016,0.0001094349,0.005132947,0.00004961873,0.01033614,0.0005813999,0.0003424973],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861339,0.0008150942,0.005858845,0.00284696,0.000958339,0.0001893825,0.000003818013,0.000391748,0.002801857],"genre_scores_gemma":[0.9880003,0.0001850691,0.006543225,0.0004425045,0.00007513555,0.00003840427,0.000008760028,0.00001725118,0.004689365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03595597,"threshold_uncertainty_score":0.9999365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05872274225453371,"score_gpt":0.2787022660423309,"score_spread":0.2199795237877972,"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."}}