{"id":"W4394380841","doi":"10.6084/m9.figshare.6813131","title":"Machine learning databases used for Journal of Geophysical Research: Space Physics manuscript: \"New capabilities for prediction of high-latitude ionospheric scintillation: A novel approach with machine learning.\"","year":2018,"lang":"en","type":"dataset","venue":"Figshare","topic":"Earthquake Detection and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ionosphere; Space (punctuation); Space weather; Scintillation; High latitude; Computer science; Database; Latitude; Geophysics; Physics; Astronomy; Telecommunications","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":[0.0004967842,0.0003046221,0.0006402468,0.0002201622,0.0004032758,0.0001210637,0.0003634989,0.0001488628,0.01351205],"category_scores_gemma":[0.0015861,0.0002371937,0.0002462817,0.0006022227,0.00009068509,0.0003609436,0.00004008462,0.0007585757,0.00004399849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001771558,"about_ca_system_score_gemma":0.0003058808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003288853,"about_ca_topic_score_gemma":0.002443874,"domain_scores_codex":[0.9976547,0.0001820631,0.0005130369,0.0004485716,0.0008423069,0.000359366],"domain_scores_gemma":[0.9970176,0.0007954272,0.0008367962,0.000328856,0.0008494012,0.0001718926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002962107,0.00009687372,0.001151168,0.001209193,0.0002496034,0.000001255529,0.0001083159,0.06434283,0.000006270802,0.000002694105,0.9317371,0.0007984808],"study_design_scores_gemma":[0.001142377,0.001755882,0.002600635,0.0008363724,0.0001881035,0.00001746208,0.0001189559,0.1396514,0.00006740709,0.00002679791,0.8533424,0.0002521236],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003021623,0.0003675757,0.002256178,0.00003064973,0.0001025846,0.000528272,0.9963785,0.00002330665,0.00001077373],"genre_scores_gemma":[0.005698827,0.00003416669,0.00575609,0.00001073668,0.001226225,0.00001802556,0.9866719,0.00001734063,0.0005666742],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07839467,"threshold_uncertainty_score":0.9873897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.111673611253554,"score_gpt":0.2809746145648109,"score_spread":0.1693010033112569,"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."}}