{"id":"W4393427597","doi":"10.5281/zenodo.7309505","title":"SuperDARN data in netCDF format (2020-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; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001018196,0.001367009,0.0009002438,0.002998288,0.0007148914,0.00296283,0.001984472,0.001700171,0.3181053],"category_scores_gemma":[0.004698225,0.0006593374,0.001048697,0.004369596,0.0003025058,0.002773129,0.002013962,0.001801025,0.3307123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001251711,"about_ca_system_score_gemma":0.001636128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0162972,"about_ca_topic_score_gemma":0.01961319,"domain_scores_codex":[0.999243,0.00007428235,0.00008696289,0.0001586096,0.0002913543,0.0001458235],"domain_scores_gemma":[0.9978618,0.0003023985,0.0001608167,0.0005077177,0.00100487,0.0001623828],"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":[0.00004280384,0.00001149829,0.0003840548,0.0001602735,0.000007788024,0.00001674193,0.00001410068,0.0002081807,0.0001502342,0.0004666453,0.995092,0.003445513],"study_design_scores_gemma":[0.00007153692,0.000007027946,0.001869931,0.0001113999,0.000006373876,0.00003109537,0.00006212218,0.0002750578,0.0004983916,0.001349787,0.9956967,0.00002066109],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001856815,0.00003043297,0.0004979768,0.000132437,0.0001225462,0.00002729977,0.9916151,0.00238179,0.005006733],"genre_scores_gemma":[0.0007627112,0.00004925457,0.001500509,0.0001093328,0.0000308309,0.00007593001,0.9921463,0.001348178,0.003976942],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3181053,"threshold_uncertainty_score":0.97264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06288992937073092,"score_gpt":0.3208665296603924,"score_spread":0.2579766002896615,"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."}}