{"id":"W4393922350","doi":"10.5281/zenodo.6344019","title":"SuperDARN data in netCDF format (2016-Nov)","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); 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":[0.0009426797,0.001455126,0.0009600738,0.002853189,0.0006810893,0.002372303,0.00217574,0.001594858,0.1332265],"category_scores_gemma":[0.005005539,0.0005466734,0.001170095,0.004188061,0.0003419673,0.001964793,0.001962745,0.001737705,0.2046902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001424183,"about_ca_system_score_gemma":0.001837408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01739118,"about_ca_topic_score_gemma":0.02780187,"domain_scores_codex":[0.9992083,0.00009757291,0.0001102719,0.000219057,0.0002308286,0.0001340321],"domain_scores_gemma":[0.9981694,0.0003182918,0.0001501037,0.0005086457,0.0007004681,0.0001530699],"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":[0.00003287205,0.00001015363,0.0004673412,0.0002525972,0.00001230804,0.00001594888,0.00001476176,0.0001814406,0.0001009094,0.0003949721,0.9961621,0.002354737],"study_design_scores_gemma":[0.00006300469,0.000006145483,0.002124253,0.0001532525,0.000009140899,0.0000389373,0.00006286256,0.0002169417,0.0003409514,0.001263648,0.9957034,0.00001744033],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000100256,0.00003291048,0.0001821035,0.00006360465,0.00004470083,0.0000112822,0.9975975,0.0007616772,0.001206026],"genre_scores_gemma":[0.0003054195,0.00003494071,0.0004855864,0.00004422087,0.000008877636,0.00003990042,0.9978642,0.0002272474,0.0009897022],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1332265,"threshold_uncertainty_score":0.4456869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07272571633909522,"score_gpt":0.3227311148170907,"score_spread":0.2500053984779954,"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."}}