{"id":"W4393750657","doi":"10.5281/zenodo.6213188","title":"SuperDARN data in netCDF format (2017-Dec)","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"NetCDF; Computer science; Computer graphics (images); Database; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009703754,0.001522559,0.001005504,0.003026482,0.0006693068,0.002485372,0.00226771,0.001661415,0.143674],"category_scores_gemma":[0.005214463,0.0005602828,0.001183394,0.004470198,0.0003510416,0.002023043,0.002026207,0.001818176,0.2145663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001452566,"about_ca_system_score_gemma":0.001864502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01626206,"about_ca_topic_score_gemma":0.02533953,"domain_scores_codex":[0.9991758,0.00010562,0.0001133633,0.0002260205,0.0002368399,0.0001423418],"domain_scores_gemma":[0.9981148,0.0003563688,0.0001540168,0.0005088514,0.000712564,0.0001533953],"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":[0.00003002949,0.000009889039,0.0004274977,0.0002520913,0.00001227637,0.00001486125,0.00001371654,0.0001806077,0.00009087566,0.0004180061,0.9963261,0.002224095],"study_design_scores_gemma":[0.00006583676,0.000006126764,0.001871981,0.0001609141,0.000009489499,0.00003874476,0.00006604586,0.0002386105,0.0003379241,0.001446143,0.9957405,0.00001781291],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009174315,0.00003412179,0.0001965603,0.0000667529,0.00004471464,0.00001157088,0.9976089,0.0007892735,0.001156312],"genre_scores_gemma":[0.0002997474,0.00003737608,0.0005064404,0.00004780091,0.000009274108,0.00004358569,0.9978694,0.0002456848,0.0009407738],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.143674,"threshold_uncertainty_score":0.4806374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09316381517977354,"score_gpt":0.3342067274022956,"score_spread":0.2410429122225221,"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."}}