{"id":"W3128773410","doi":"","title":"Improvement of SuperDARN Data Products by Signal-Derived Weights","year":2015,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"SIGNAL (programming language); Geology; Computer science; Remote sensing; Geodesy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001328461,0.001816416,0.0005114044,0.001543082,0.000337027,0.001421075,0.0007662434,0.0006097343,0.006517109],"category_scores_gemma":[0.006133524,0.0005211279,0.0007174655,0.00211041,0.0002266629,0.002431656,0.001531517,0.001087932,0.004856129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000306658,"about_ca_system_score_gemma":0.0009171771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002886129,"about_ca_topic_score_gemma":0.00373166,"domain_scores_codex":[0.9992725,0.0001025865,0.0000622545,0.0001413537,0.0003516017,0.00006954258],"domain_scores_gemma":[0.9975352,0.0002846071,0.0001573641,0.0005380847,0.00142153,0.00006328021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001111252,0.0002236174,0.00674319,0.000298996,0.0001512877,0.0001180263,0.0001789633,0.0589524,0.1547493,0.00608252,0.01177043,0.7596201],"study_design_scores_gemma":[0.0001931548,0.0002354085,0.01760749,0.00007535526,0.0001989545,0.0002127266,0.0001301784,0.7716125,0.1569317,0.004775985,0.04789121,0.000135377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1235552,0.0004879812,0.8526357,0.0004478343,0.0005252076,0.0001101277,0.003603702,0.009707784,0.008926465],"genre_scores_gemma":[0.2241262,0.0004092386,0.7573169,0.0001666438,0.0001024551,0.00009338638,0.009823793,0.002304447,0.005656829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006517109,"threshold_uncertainty_score":0.02180189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.036088568548658,"score_gpt":0.2342778513672059,"score_spread":0.1981892828185479,"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."}}