{"id":"W4393802636","doi":"10.5281/zenodo.6579565","title":"SuperDARN data in netCDF format (2008-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; 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":[0.0009145575,0.001449138,0.0009784875,0.003103998,0.0006653484,0.002336545,0.00221151,0.001602763,0.1355796],"category_scores_gemma":[0.004867302,0.0005727229,0.001144911,0.004782257,0.0003175978,0.001918688,0.001808657,0.001742799,0.2025858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001478667,"about_ca_system_score_gemma":0.001790508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01800743,"about_ca_topic_score_gemma":0.02696128,"domain_scores_codex":[0.9992151,0.00009517623,0.000107497,0.0002175004,0.000232029,0.0001326667],"domain_scores_gemma":[0.9981633,0.0003348677,0.0001516595,0.0005094874,0.000700397,0.0001403012],"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":[0.00002785537,0.000009615456,0.0004010835,0.000216601,0.0000108433,0.00001393496,0.00001306978,0.0001741592,0.00008848316,0.0003779404,0.9964744,0.002191932],"study_design_scores_gemma":[0.00006247216,0.000005825108,0.002107283,0.0001394344,0.000008844599,0.00003676277,0.00005929095,0.0002381547,0.0003273538,0.001203193,0.9957941,0.00001740204],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009514103,0.00002743123,0.0001760467,0.00005773556,0.00003553235,0.0000104891,0.9976897,0.0007394308,0.001168601],"genre_scores_gemma":[0.0002766188,0.00003034312,0.0004848532,0.00003936248,0.000007515728,0.0000396225,0.9979354,0.000221708,0.0009644995],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1355796,"threshold_uncertainty_score":0.4535591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07153476680243331,"score_gpt":0.3224539595428085,"score_spread":0.2509191927403752,"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."}}