{"id":"W4393467006","doi":"10.5281/zenodo.6575260","title":"SuperDARN data in netCDF format (2009-Nov)","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 graphics (images); Geology; Computer science; 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":[0.0009451332,0.001460853,0.0009767718,0.003013151,0.0006948304,0.002313326,0.002256448,0.001662919,0.1286982],"category_scores_gemma":[0.004863387,0.000572507,0.001147103,0.004506478,0.000327314,0.001923438,0.001859802,0.001776107,0.1946463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001530929,"about_ca_system_score_gemma":0.001830724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0186541,"about_ca_topic_score_gemma":0.02899798,"domain_scores_codex":[0.9991737,0.00009799701,0.000114982,0.0002313352,0.0002429774,0.0001390425],"domain_scores_gemma":[0.9980786,0.0003346756,0.000162678,0.0005554457,0.0007183588,0.0001502665],"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":[0.00003136169,0.00001046478,0.0004552654,0.0002286365,0.00001179207,0.000015728,0.00001332292,0.0001868358,0.0001035893,0.0003948481,0.9963258,0.002222333],"study_design_scores_gemma":[0.00006288404,0.000006020392,0.002370363,0.000142077,0.000009232047,0.00004039636,0.00005951674,0.0002397368,0.0003747951,0.00120094,0.9954756,0.00001846886],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009837664,0.00002705745,0.0001763822,0.0000560915,0.00003681691,0.00001034068,0.9977132,0.0006967901,0.001184916],"genre_scores_gemma":[0.0002829337,0.00002846427,0.0004901443,0.00003925286,0.000007188875,0.00003595744,0.998001,0.0002085964,0.0009063148],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1286982,"threshold_uncertainty_score":0.4305385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0721804041816796,"score_gpt":0.3235884387106946,"score_spread":0.251408034529015,"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."}}