{"id":"W4393609246","doi":"10.5281/zenodo.6802184","title":"SuperDARN data in netCDF format (2000-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 science; Database; Computer graphics (images); Geology; 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":[0.0009683085,0.001505045,0.000981994,0.003247203,0.0006763851,0.002362391,0.002285796,0.001650742,0.1339189],"category_scores_gemma":[0.00486769,0.0005829806,0.001125806,0.004891397,0.0003307311,0.001894432,0.001857131,0.0017703,0.2024417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001450569,"about_ca_system_score_gemma":0.001793565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01674021,"about_ca_topic_score_gemma":0.02516478,"domain_scores_codex":[0.9991934,0.0000981851,0.0001096941,0.0002263567,0.0002373648,0.0001349823],"domain_scores_gemma":[0.9980842,0.0003421284,0.0001634038,0.0005521558,0.0007036047,0.0001544967],"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":[0.00003403642,0.00001127557,0.000459432,0.0002566996,0.00001211512,0.00001629094,0.00001474251,0.0002033308,0.000120717,0.0004374186,0.9959169,0.002516984],"study_design_scores_gemma":[0.00006250003,0.000006059984,0.00213374,0.0001383047,0.000009076437,0.00003856388,0.00005773762,0.0002392455,0.0003796627,0.001234025,0.9956837,0.00001741598],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000102875,0.00002863348,0.0002092886,0.00005405119,0.00003544852,0.0000116691,0.9974662,0.0008075511,0.001284237],"genre_scores_gemma":[0.0002872929,0.00002955309,0.0005408101,0.00003998498,0.000007358254,0.0000396377,0.9978803,0.0002449314,0.0009301722],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1339189,"threshold_uncertainty_score":0.4480033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07089532048450363,"score_gpt":0.3216434616977507,"score_spread":0.250748141213247,"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."}}