{"id":"W4393488211","doi":"10.5281/zenodo.6800163","title":"SuperDARN data in netCDF format (2000-Dec)","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":[0.0009596166,0.001520734,0.000987958,0.003292153,0.0006548499,0.002376911,0.002261982,0.001628094,0.1388291],"category_scores_gemma":[0.004911289,0.0005720636,0.001102821,0.005026055,0.0003268923,0.001910223,0.001850325,0.001763636,0.2054098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431041,"about_ca_system_score_gemma":0.001754392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01610432,"about_ca_topic_score_gemma":0.02372887,"domain_scores_codex":[0.9991894,0.0001009268,0.0001112225,0.0002294919,0.0002348092,0.0001341597],"domain_scores_gemma":[0.9980995,0.0003490706,0.0001636442,0.000534161,0.0007028539,0.0001508835],"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":[0.00003231821,0.00001082229,0.0004482357,0.0002596984,0.00001218657,0.00001552918,0.00001423769,0.0002018111,0.0001142801,0.0004518734,0.9959175,0.002521613],"study_design_scores_gemma":[0.00006074811,0.000005913601,0.001977979,0.0001422196,0.000009090712,0.00003745582,0.00005918038,0.0002417918,0.0003658101,0.001325216,0.9957576,0.00001696876],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009492816,0.00002939966,0.0002139098,0.00005511853,0.00003488301,0.00001137823,0.9975388,0.0007918609,0.001229715],"genre_scores_gemma":[0.000298238,0.00003226864,0.000555727,0.00004256935,0.000007700551,0.00004104104,0.9978364,0.0002481357,0.0009379751],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1388291,"threshold_uncertainty_score":0.4644297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07212235598433839,"score_gpt":0.3222771178026503,"score_spread":0.2501547618183119,"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."}}