{"id":"W4393838362","doi":"10.5281/zenodo.6578537","title":"SuperDARN data in netCDF format (2009-Aug)","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); Database; 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":[0.000924949,0.001457958,0.0009849543,0.003112372,0.0006916453,0.002291204,0.002256386,0.00164702,0.1287237],"category_scores_gemma":[0.004668179,0.0005564152,0.001134011,0.004731583,0.0003219073,0.001879518,0.001829422,0.001756312,0.1904388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001528402,"about_ca_system_score_gemma":0.001775618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01954468,"about_ca_topic_score_gemma":0.02997065,"domain_scores_codex":[0.9992055,0.0000954116,0.0001078715,0.0002232129,0.0002335778,0.0001343811],"domain_scores_gemma":[0.9981774,0.0003198017,0.0001563406,0.0005152841,0.0006900553,0.0001411252],"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":[0.00003051366,0.00001027205,0.0004511629,0.0002273727,0.0000118551,0.00001551493,0.00001329031,0.000190276,0.00009858076,0.0003933284,0.996354,0.002203836],"study_design_scores_gemma":[0.00006326886,0.000005811686,0.002361367,0.0001425872,0.000009416579,0.00003924065,0.00006095321,0.0002383681,0.0003486947,0.001213458,0.9954987,0.00001805886],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000959629,0.00002873878,0.000169054,0.00005581464,0.00003534755,0.000009939285,0.9977984,0.0006664874,0.001140202],"genre_scores_gemma":[0.0002914226,0.00003009821,0.0004830949,0.00003930416,0.0000074149,0.00003618557,0.9980263,0.0001993607,0.0008867562],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1287237,"threshold_uncertainty_score":0.4306238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07079965075190249,"score_gpt":0.3230157318783631,"score_spread":0.2522160811264606,"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."}}