{"id":"W2736909352","doi":"10.1002/2017sw001631","title":"The Tsallis statistical distribution applied to geomagnetically induced currents","year":2017,"lang":"en","type":"article","venue":"Space Weather","topic":"Earthquake Detection and Analysis","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Natural Environment Research Council; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Sight Research UK","keywords":"Exponential function; Cumulative distribution function; Range (aeronautics); Statistical physics; Exponential distribution; Physics; Distribution function; Statistics; Mathematics; Meteorology; Probability density function; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004301419,0.0004768205,0.0005996467,0.002033043,0.0005469999,0.001041728,0.0009767928,0.0006436678,0.002822777],"category_scores_gemma":[0.02235196,0.0002612855,0.001208143,0.00150306,0.001637866,0.001380535,0.000669059,0.0008998694,0.0004747402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001217462,"about_ca_system_score_gemma":0.001030368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01156419,"about_ca_topic_score_gemma":0.00330276,"domain_scores_codex":[0.9986947,0.0005310483,0.00008511434,0.0002539514,0.0002730608,0.0001620692],"domain_scores_gemma":[0.9839129,0.01286934,0.001335557,0.0008514458,0.0007524452,0.0002782707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000218944,0.0001432676,0.09245776,0.0001474094,0.0003268154,0.001219451,0.0005497211,0.7309373,0.00513747,0.1218265,0.002246572,0.04478876],"study_design_scores_gemma":[0.000009548886,0.00005867616,0.01214166,0.00001229593,0.00001671794,0.0002321081,0.00009713085,0.9687896,0.0006858353,0.01744245,0.0004936669,0.00002022028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3437536,0.0003377993,0.6508125,0.0003110667,0.00007910154,0.0001293928,0.000379582,0.0007299228,0.00346701],"genre_scores_gemma":[0.9878415,0.0001413886,0.01072711,0.00004745097,0.00005881639,0.00008557782,0.0002824436,0.00005099375,0.0007647079],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01156419,"threshold_uncertainty_score":0.02299374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01310945862767873,"score_gpt":0.2458849885529117,"score_spread":0.232775529925233,"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."}}