{"id":"W4393441511","doi":"10.5281/zenodo.6581304","title":"SuperDARN data in netCDF format (2008-Jan)","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); 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":[0.0009311603,0.001452103,0.0009866919,0.00313171,0.0006729862,0.002386705,0.00225195,0.001652672,0.1369185],"category_scores_gemma":[0.004983031,0.0005902664,0.001143019,0.004786037,0.0003152562,0.00196094,0.001865456,0.001754371,0.2025374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001491757,"about_ca_system_score_gemma":0.001804489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01777931,"about_ca_topic_score_gemma":0.02633796,"domain_scores_codex":[0.9992076,0.00009735674,0.0001092923,0.0002228622,0.0002300838,0.0001327983],"domain_scores_gemma":[0.9981492,0.000345433,0.0001565431,0.0005144319,0.0006911606,0.000143249],"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":[0.00002769268,0.000009240965,0.0003920545,0.0002150283,0.00001059939,0.00001379336,0.00001297713,0.0001645528,0.00008412427,0.0003918493,0.9965246,0.002153495],"study_design_scores_gemma":[0.00006044986,0.000005524349,0.001986644,0.0001402054,0.000008657758,0.00003535943,0.00005679058,0.0002220807,0.0003074298,0.001203141,0.9959572,0.00001658103],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009061806,0.00002706696,0.0001769687,0.0000593933,0.00003464477,0.00001037141,0.9976948,0.0007388568,0.001167258],"genre_scores_gemma":[0.0002766725,0.00003108869,0.000499377,0.00004118072,0.000007579732,0.0000419935,0.9978991,0.0002280652,0.0009748923],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1369185,"threshold_uncertainty_score":0.458038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07051040205294502,"score_gpt":0.3218158785856529,"score_spread":0.2513054765327078,"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."}}