{"id":"W4393790458","doi":"10.5281/zenodo.7652537","title":"SuperDARN Grid data in netCDF format (2000-Jun)","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"NetCDF; Grid; Computer science; Geology; Geodesy; 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":[0.0009327093,0.001165694,0.0009075649,0.003089789,0.0008276154,0.002787848,0.002060945,0.001384713,0.2684652],"category_scores_gemma":[0.004358989,0.0007506533,0.0009705112,0.005941526,0.0002739661,0.002332294,0.001710701,0.001932932,0.2623641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001381438,"about_ca_system_score_gemma":0.001823904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02426234,"about_ca_topic_score_gemma":0.02700266,"domain_scores_codex":[0.9992718,0.00006304383,0.00007775466,0.0001445335,0.0003043687,0.0001384896],"domain_scores_gemma":[0.9979266,0.0002372152,0.0001482341,0.000544377,0.0009706836,0.0001727913],"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":[0.00005248045,0.00001248926,0.0004320142,0.000120168,0.000008850495,0.00001874246,0.00001713202,0.0002618243,0.0001692534,0.0006330308,0.9949548,0.003319327],"study_design_scores_gemma":[0.00008599567,0.000006293627,0.002452231,0.00007993654,0.000007142688,0.00002803661,0.00006174756,0.0003450331,0.0005725622,0.001529254,0.9948114,0.00002040223],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002950692,0.00002804001,0.0006595633,0.000131517,0.0001428094,0.0000294085,0.9880906,0.002763284,0.007859565],"genre_scores_gemma":[0.0009506943,0.00004105701,0.001669785,0.00007753848,0.00002438875,0.00006571462,0.9912106,0.001595752,0.004364549],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2684652,"threshold_uncertainty_score":0.8981056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06329746246529759,"score_gpt":0.273243315368223,"score_spread":0.2099458529029254,"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."}}