{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002193274,0.001743729,0.0009357441,0.004163153,0.00039725,0.002603258,0.001765396,0.0009617667,0.03758626],"category_scores_gemma":[0.01038636,0.0008638099,0.001893605,0.00315526,0.0003500558,0.002269554,0.001853626,0.001476047,0.03750985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002816369,"about_ca_system_score_gemma":0.0008305083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001406027,"about_ca_topic_score_gemma":0.001666461,"domain_scores_codex":[0.9991309,0.0001522362,0.0001107991,0.0001960502,0.0003399,0.00007014225],"domain_scores_gemma":[0.9981552,0.0009255256,0.0001844563,0.0002738879,0.000408662,0.0000524002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004058828,0.0001188926,0.002002164,0.001910564,0.0003658245,0.0005017326,0.0004333389,0.01168024,0.02331248,0.01035588,0.2560572,0.6928558],"study_design_scores_gemma":[0.0003554541,0.000281409,0.01060904,0.0006647221,0.0003246432,0.001154594,0.0005025573,0.3731078,0.06548626,0.05216451,0.4950249,0.0003241438],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.005487281,0.0008116663,0.8853394,0.000254269,0.0003778199,0.0002628567,0.0218624,0.0815698,0.004034356],"genre_scores_gemma":[0.0305498,0.001784518,0.8759147,0.0001780473,0.0001591491,0.001326165,0.05597561,0.02497129,0.00914068],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.03758626,"threshold_uncertainty_score":0.1257385,"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."}}