{"id":"W4393521648","doi":"10.5281/zenodo.6592008","title":"SuperDARN data in netCDF format (2007-Jul)","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":[0.000941882,0.001494506,0.001008732,0.003228649,0.000688382,0.002458315,0.002286436,0.001662876,0.1379092],"category_scores_gemma":[0.004836586,0.0005894987,0.001190752,0.005127836,0.0003254514,0.001930999,0.001837665,0.001831266,0.2060912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001559649,"about_ca_system_score_gemma":0.00195208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01917206,"about_ca_topic_score_gemma":0.02861155,"domain_scores_codex":[0.9991875,0.00009780193,0.0001102732,0.0002174631,0.0002466671,0.0001401838],"domain_scores_gemma":[0.9981329,0.0003373426,0.0001514754,0.0005065017,0.000723848,0.0001480249],"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":[0.00002659912,0.000009638656,0.0003747804,0.0002089694,0.0000109571,0.00001371098,0.00001214885,0.0001766206,0.00008444208,0.0003835314,0.9966539,0.00204478],"study_design_scores_gemma":[0.00006413972,0.00000569859,0.002058492,0.0001394896,0.00000904758,0.00003711556,0.00005934602,0.00024395,0.0003375736,0.001232447,0.9957945,0.00001814102],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000855188,0.00002677606,0.0001682191,0.00005538253,0.00003474878,0.00001006634,0.9977998,0.000690593,0.001128866],"genre_scores_gemma":[0.0002419225,0.00002964058,0.0004518322,0.0000378189,0.000007082864,0.00003620315,0.9981114,0.0002103094,0.000873902],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1379092,"threshold_uncertainty_score":0.4613522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06977290491306498,"score_gpt":0.3230868376106549,"score_spread":0.2533139326975899,"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."}}