{"id":"W4393420472","doi":"10.5281/zenodo.6570616","title":"SuperDARN data in netCDF format (2010-Oct)","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":[0.0008948837,0.001491799,0.0009671343,0.003001577,0.0006674881,0.002289875,0.002144212,0.001623549,0.1278887],"category_scores_gemma":[0.004635882,0.0005495538,0.001115084,0.004686092,0.0003292199,0.001920572,0.001803033,0.001704199,0.1936335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001536048,"about_ca_system_score_gemma":0.001903311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02065976,"about_ca_topic_score_gemma":0.03244516,"domain_scores_codex":[0.9992169,0.00009383622,0.0001047667,0.0002185323,0.0002299743,0.0001358384],"domain_scores_gemma":[0.9981937,0.0003071039,0.0001499109,0.0004874478,0.0007131975,0.0001486223],"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":[0.00003073943,0.00001001076,0.000440688,0.0002340352,0.00001151309,0.00001475057,0.00001325612,0.0001902987,0.00009864632,0.0003799539,0.9962828,0.00229331],"study_design_scores_gemma":[0.00006200733,0.00000618574,0.002338328,0.0001491354,0.000009478143,0.00003888051,0.00006313331,0.0002512224,0.0003578788,0.001191385,0.995514,0.00001835324],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000945914,0.00002896326,0.0001670492,0.00005710695,0.00003480752,0.00001004045,0.9977704,0.0006936845,0.001143375],"genre_scores_gemma":[0.0002911018,0.00003114754,0.0004536261,0.00004014625,0.000007472309,0.00003535108,0.9980313,0.0001994602,0.0009103292],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1278887,"threshold_uncertainty_score":0.4278303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08203074605685161,"score_gpt":0.3223067134980733,"score_spread":0.2402759674412216,"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."}}