{"id":"W4393747348","doi":"10.5281/zenodo.7613365","title":"SuperDARN data in netCDF format (2021-Sep)","year":2023,"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; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001026758,0.001443652,0.001004761,0.002967904,0.0008096794,0.003020668,0.002172827,0.001801856,0.3638018],"category_scores_gemma":[0.004986604,0.0007314075,0.001150718,0.004282285,0.0003219257,0.002848191,0.002202355,0.001899261,0.3678862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001274202,"about_ca_system_score_gemma":0.001698778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01624493,"about_ca_topic_score_gemma":0.01984274,"domain_scores_codex":[0.9992262,0.00007586372,0.00008866846,0.0001634403,0.0002922516,0.0001537175],"domain_scores_gemma":[0.9978061,0.0003396869,0.0001534967,0.0005319178,0.001005253,0.0001636563],"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":[0.00004260781,0.00001182522,0.0003702161,0.0001711528,0.000008215576,0.00001848089,0.0000150439,0.0002078705,0.0001566615,0.0004679713,0.9950663,0.003463587],"study_design_scores_gemma":[0.00007782331,0.000007453597,0.001888719,0.0001238574,0.000007000898,0.00003462672,0.00006588415,0.0003122983,0.0005687843,0.001641603,0.9952492,0.00002290529],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000181481,0.00002987632,0.0005835115,0.000126908,0.0001111201,0.0000295604,0.9912821,0.002712706,0.00494274],"genre_scores_gemma":[0.0007656388,0.00004657026,0.001562524,0.0001078332,0.00002900781,0.00008572937,0.9919834,0.001586067,0.003833335],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3638018,"threshold_uncertainty_score":0.9074595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08511811373846183,"score_gpt":0.3371008175428913,"score_spread":0.2519827038044295,"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."}}