{"id":"W4393456646","doi":"10.5281/zenodo.6798274","title":"SuperDARN data in netCDF format (2001-Dec)","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":[],"consensus_categories":[],"category_scores_codex":[0.0009889214,0.001525984,0.0009934183,0.003447563,0.0006833486,0.002440439,0.002278698,0.001706813,0.1354105],"category_scores_gemma":[0.005205413,0.0005995558,0.001111569,0.005603016,0.0003094008,0.001940424,0.001742183,0.001834616,0.2001557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001581213,"about_ca_system_score_gemma":0.00185694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01817247,"about_ca_topic_score_gemma":0.02517211,"domain_scores_codex":[0.999136,0.0001050978,0.0001248417,0.0002382427,0.0002546773,0.0001412431],"domain_scores_gemma":[0.9978685,0.0003893177,0.0001880671,0.0005875585,0.0008045849,0.0001618755],"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":[0.00003204009,0.00001081123,0.0004468662,0.0002396432,0.00001164735,0.00001585161,0.00001371848,0.00020076,0.00009948525,0.0004238675,0.9961706,0.00233466],"study_design_scores_gemma":[0.00006096418,0.000005891438,0.002262381,0.0001413041,0.000009085702,0.00003827785,0.00005989882,0.0002358406,0.0003429279,0.00116444,0.9956616,0.00001737149],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009059597,0.00002497779,0.0001687414,0.00005232292,0.00003136854,0.000009793073,0.9979129,0.0006072093,0.001102141],"genre_scores_gemma":[0.0002594196,0.00002751134,0.000429508,0.00003490532,0.000006554588,0.00003540546,0.9981396,0.0001900175,0.0008770415],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1354105,"threshold_uncertainty_score":0.4529932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08635304769373679,"score_gpt":0.3311660112851096,"score_spread":0.2448129635913728,"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."}}