{"id":"W2053019804","doi":"10.1007/s10509-013-1411-8","title":"Temporal and spatial distribution of GPS-TEC anomalies prior to the strong earthquakes","year":2013,"lang":"en","type":"article","venue":"Astrophysics and Space Science","topic":"Earthquake Detection and Analysis","field":"Earth and Planetary Sciences","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Oceanic and Atmospheric Administration; National Natural Science Foundation of China; U.S. Geological Survey; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Epicenter; TEC; Ionosphere; Seismology; Geology; Anomaly (physics); Total electron content; Geodesy; Longitude; Global Positioning System; Magnitude (astronomy); Amplitude; Spatial distribution; Latitude; Geophysics; Physics; Remote sensing; Astronomy","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":[0.0001813077,0.00008210335,0.0001080102,0.0000422328,0.0003955989,0.0001944568,0.0001351167,0.00001518766,0.0001052981],"category_scores_gemma":[0.00002760771,0.00005289808,0.00002550599,0.0004065736,0.0003983651,0.0003014164,0.00002874592,0.00005446762,0.0000265706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000155513,"about_ca_system_score_gemma":0.00003084155,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007495679,"about_ca_topic_score_gemma":0.003002984,"domain_scores_codex":[0.9992015,0.00002367157,0.0001071107,0.0002155897,0.0002546902,0.0001974149],"domain_scores_gemma":[0.9995719,0.00002927639,0.00006019109,0.0001381237,0.00007375154,0.0001267237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001144574,0.00001221254,0.2438928,0.000007229275,0.000009361343,6.38506e-7,0.0003692173,0.00107455,0.003366684,0.0004268905,0.00008921813,0.7507398],"study_design_scores_gemma":[0.00006455855,0.0001592112,0.9744903,0.000006261421,0.000007464741,0.00000198944,0.0005665112,0.02272828,0.0008673917,0.00009151955,0.0009400816,0.00007638249],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991861,0.00004843398,0.006799013,0.0009203266,0.00005717651,0.0001107722,0.00004688483,0.000009011896,0.0001473694],"genre_scores_gemma":[0.9991944,0.00001408929,0.0006127448,0.00003236635,0.00004555126,8.497942e-7,0.00001096578,0.000001160831,0.00008787168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7506634,"threshold_uncertainty_score":0.9991135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005870190865184031,"score_gpt":0.1911630378015512,"score_spread":0.1852928469363671,"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."}}