{"id":"W4393711128","doi":"10.5281/zenodo.6427542","title":"SuperDARN data in netCDF format (2015-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":[0.0009929555,0.001441583,0.000942828,0.002989531,0.0006759124,0.002375646,0.002214467,0.001620785,0.1298815],"category_scores_gemma":[0.005173218,0.0005328361,0.00114128,0.004283147,0.0003410558,0.001931349,0.001957018,0.001708683,0.1904129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001446814,"about_ca_system_score_gemma":0.001798479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01735209,"about_ca_topic_score_gemma":0.02787396,"domain_scores_codex":[0.9991871,0.0001064041,0.0001129117,0.0002233289,0.0002342374,0.0001359676],"domain_scores_gemma":[0.9981391,0.0003466068,0.0001570354,0.0005022456,0.0007049018,0.0001500878],"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":[0.00003112428,0.00001042314,0.0004639687,0.0002593058,0.00001257992,0.00001552793,0.00001495645,0.0001891736,0.00009724766,0.0004105947,0.9962203,0.002274799],"study_design_scores_gemma":[0.00006575758,0.000006548195,0.002244425,0.0001611902,0.000009845805,0.00003929401,0.00006597606,0.0002334929,0.0003321159,0.001302978,0.9955205,0.00001791432],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001042827,0.00003514121,0.0001869882,0.00006585384,0.00004254038,0.00001173426,0.9976358,0.0007614569,0.001156112],"genre_scores_gemma":[0.0003245874,0.00003607176,0.0005145568,0.00004754018,0.00000904496,0.00004227143,0.9978466,0.0002155967,0.0009636308],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1298815,"threshold_uncertainty_score":0.4344971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07340204060415305,"score_gpt":0.3335801675388471,"score_spread":0.260178126934694,"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."}}