{"id":"W4393656048","doi":"10.5281/zenodo.7140340","title":"Wallops SuperDARN data in netCDF format (2022-Sep)","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; Geology; Computer science; 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":[0.001062334,0.001441479,0.001007806,0.00278799,0.0009822223,0.003333909,0.00229499,0.001783935,0.3190106],"category_scores_gemma":[0.00483998,0.0008685108,0.001053649,0.004721003,0.0003364285,0.002906712,0.002154107,0.002254416,0.3496788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001436775,"about_ca_system_score_gemma":0.002056506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02441054,"about_ca_topic_score_gemma":0.02543363,"domain_scores_codex":[0.999157,0.00007447087,0.00008614797,0.0001741814,0.0003240397,0.0001842303],"domain_scores_gemma":[0.9976541,0.0002720821,0.0002028401,0.0005808571,0.001076864,0.0002131578],"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":[0.0000444362,0.00001105632,0.0003902873,0.0001307664,0.000008652916,0.00002056713,0.00001563074,0.0001810025,0.0001584449,0.0003824305,0.9959985,0.002658113],"study_design_scores_gemma":[0.0001015473,0.000008631691,0.002567235,0.0001297989,0.000008234171,0.00002811336,0.00008216198,0.0002563765,0.000562966,0.001067013,0.9951618,0.0000260446],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001947551,0.00002333063,0.0003213199,0.0001111936,0.0001198328,0.00002238064,0.9923859,0.00143706,0.005384266],"genre_scores_gemma":[0.0006913209,0.00003597173,0.0009585668,0.00009097677,0.00002757362,0.00007328372,0.9934314,0.001125986,0.003564938],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3190106,"threshold_uncertainty_score":0.9713488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06456588112974665,"score_gpt":0.3192351484123581,"score_spread":0.2546692672826115,"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."}}