{"id":"W4393873934","doi":"10.5281/zenodo.6592029","title":"SuperDARN data in netCDF format (2007-May)","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 graphics (images); Computer science; Geology; 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":[0.0009861952,0.001464641,0.001004584,0.003299531,0.0007019613,0.002463578,0.002330274,0.001677535,0.1326697],"category_scores_gemma":[0.004974109,0.0005791197,0.001177159,0.005183876,0.0003281863,0.001949508,0.001854473,0.001812878,0.1930283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001607946,"about_ca_system_score_gemma":0.001980856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01987039,"about_ca_topic_score_gemma":0.03034919,"domain_scores_codex":[0.9991654,0.0001043225,0.0001120905,0.0002250129,0.0002495862,0.0001436419],"domain_scores_gemma":[0.9980362,0.0003506161,0.0001668513,0.0005281055,0.0007608021,0.0001574515],"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":[0.00002892038,0.000009955452,0.000413696,0.0002174165,0.00001135974,0.00001449716,0.00001307969,0.0001805987,0.00008608781,0.0004076841,0.996529,0.002087657],"study_design_scores_gemma":[0.00006084973,0.00000558368,0.002156323,0.0001441738,0.000009127604,0.00003735086,0.00006236241,0.0002306986,0.0003264666,0.001221593,0.9957279,0.00001768474],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008990445,0.00002900611,0.0001717314,0.00005773276,0.0000350821,0.00001043988,0.9977736,0.0006744721,0.001157889],"genre_scores_gemma":[0.0002600735,0.00003099695,0.0004748341,0.00004050537,0.000007435708,0.00003732363,0.9980705,0.0002037269,0.0008745628],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1326697,"threshold_uncertainty_score":0.4438242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07230911406111754,"score_gpt":0.3275344715378108,"score_spread":0.2552253574766933,"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."}}