{"id":"W4393821715","doi":"10.5281/zenodo.7647836","title":"SuperDARN Grid data in netCDF format (2015-Jan)","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; Database; Computer graphics (images); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.002126491,0.0003246914,0.0003807242,0.0005847411,0.001207705,0.002689096,0.01221062,0.0002083745,0.0007279529],"category_scores_gemma":[0.0007395815,0.0003432111,0.00006259022,0.001595116,0.00009486684,0.0008021927,0.01378339,0.0007747529,0.07087171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001854818,"about_ca_system_score_gemma":0.00002104683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003543275,"about_ca_topic_score_gemma":0.000009621755,"domain_scores_codex":[0.9961137,0.0006340774,0.000599469,0.001103831,0.000793279,0.0007556751],"domain_scores_gemma":[0.9956712,0.0000765096,0.0002313674,0.003456021,0.000329315,0.0002356582],"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":[0.000008420355,0.00007827055,3.604548e-7,0.0001700005,0.00002929208,0.00009606897,0.0001438006,0.0001260887,0.000003175317,0.0002558318,0.9923178,0.006770917],"study_design_scores_gemma":[0.0003712874,0.0001058021,0.00005276065,0.0001481118,0.000008724246,0.0001530661,0.00004283664,0.006090183,9.290571e-7,0.00006487679,0.9926035,0.0003578643],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001697588,0.0001428267,0.009735073,0.0006671774,0.001208075,0.000505474,0.9836894,0.001347597,0.002687452],"genre_scores_gemma":[0.0003101144,0.0002200304,0.0001890928,0.0001196509,0.0004721116,5.516484e-8,0.9976722,0.000674654,0.0003421148],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07014376,"threshold_uncertainty_score":0.999902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06491530366568624,"score_gpt":0.2849500220325832,"score_spread":0.220034718366897,"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."}}