{"id":"W4393732245","doi":"10.5281/zenodo.6486346","title":"SuperDARN data in netCDF format (2013-Apr)","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; Database; Geology; 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":[0.001001794,0.001495393,0.0009910672,0.003088188,0.0007034236,0.002422926,0.002314563,0.001665341,0.1366244],"category_scores_gemma":[0.005187633,0.0005608762,0.001184123,0.004624226,0.0003459144,0.001949434,0.001988519,0.001824687,0.1990103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001499586,"about_ca_system_score_gemma":0.001898269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01830386,"about_ca_topic_score_gemma":0.02926396,"domain_scores_codex":[0.9991636,0.0001062772,0.0001123679,0.0002283815,0.0002471805,0.0001422571],"domain_scores_gemma":[0.9980982,0.0003546822,0.0001552117,0.0005111555,0.0007273123,0.0001534748],"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":[0.00002987891,0.00001021429,0.0004097545,0.0002457302,0.00001203185,0.00001486629,0.00001363582,0.0001790081,0.00009533625,0.0003936633,0.996416,0.002179924],"study_design_scores_gemma":[0.00006691435,0.000006104919,0.002085502,0.0001514991,0.000009718767,0.00003857839,0.00006330196,0.0002370101,0.0003431612,0.001296038,0.9956841,0.00001805713],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009387655,0.00003164283,0.0001922924,0.00006312824,0.00003972376,0.00001145005,0.9976284,0.0007908935,0.001148644],"genre_scores_gemma":[0.0002911976,0.00003447815,0.0005324595,0.00004525291,0.000008456247,0.0000427909,0.9978697,0.0002357601,0.0009398434],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1366244,"threshold_uncertainty_score":0.457054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07456412284249478,"score_gpt":0.322239646042518,"score_spread":0.2476755232000232,"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."}}