{"id":"W4399580984","doi":"10.32614/cran.package.waverider","title":"WaverideR: Extracting Signals from Wavelet Spectra","year":2023,"lang":"en","type":"dataset","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; Commonwealth Scientific and Industrial Research Organisation; Met Office; National Science Foundation","keywords":"Wavelet; Pattern recognition (psychology); Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004155257,0.0004068595,0.0006117967,0.0002385857,0.0002331375,0.0007858808,0.001941031,0.0002540407,0.00118689],"category_scores_gemma":[0.0001534014,0.0003538549,0.0003194727,0.0007140057,0.00003163729,0.000461981,0.0009404791,0.0005498629,0.007618225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005473927,"about_ca_system_score_gemma":0.0001116647,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008144097,"about_ca_topic_score_gemma":0.0006821128,"domain_scores_codex":[0.9971997,0.00006710008,0.0006260176,0.0009779888,0.0005624245,0.000566755],"domain_scores_gemma":[0.9974158,0.0005032141,0.0004231498,0.0014315,0.0000701351,0.0001561683],"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":[0.00000126005,0.00001865465,5.414094e-7,0.00001325133,0.0001193866,0.0001690954,0.00002080998,0.00003304052,0.00003901023,0.00008997354,0.9917111,0.007783868],"study_design_scores_gemma":[0.00008376965,0.00003287886,0.00002836853,0.00006835473,0.00006154878,0.000009708236,0.00004954866,0.01791884,0.0001427233,0.0008161102,0.9802816,0.0005065466],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001006675,0.0001069318,0.0302084,0.0003499548,0.0008360058,0.0001097475,0.9670832,0.0003748552,0.0009208506],"genre_scores_gemma":[0.00001149245,0.0001804529,0.02044848,0.0003137512,0.0008274016,0.000009224266,0.9759536,0.00002897971,0.002226558],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0178858,"threshold_uncertainty_score":0.9998913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02743289056675543,"score_gpt":0.2512185864413581,"score_spread":0.2237856958746026,"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."}}