{"id":"W2113286023","doi":"10.1680/jees.2013.0017","title":"Interpreting nonstationary environmental cycles as amplitude-modulated (AM) signals","year":2013,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Hydro (Canada)","funders":"Ministry of Education, India; Ministry of Environment; Ministry of Earth Sciences; Health Canada; District of Columbia Department of Health","keywords":"Amplitude; Amplitude modulation; Envelope (radar); Diel vertical migration; Modulation (music); Rhythm; Nonlinear system; Perspective (graphical); Annual cycle; Environmental science; Series (stratigraphy); SIGNAL (programming language); Computer science; Frequency modulation; Biological system; Physics; Telecommunications; Climatology; Ecology; Radio frequency; Acoustics; Geology; Radar; Biology; Artificial intelligence; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002690849,0.0001220292,0.0001784139,0.00008961493,0.00009680837,0.00005936737,0.0001672321,0.00002866561,0.0004415815],"category_scores_gemma":[0.0001715425,0.00009569815,0.00004552037,0.00007820619,0.0002813091,0.0003545154,0.00008844872,0.0001605754,0.00003136517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008170972,"about_ca_system_score_gemma":0.000009992129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003876947,"about_ca_topic_score_gemma":1.405244e-8,"domain_scores_codex":[0.9988647,0.00001321267,0.0003389824,0.00014788,0.000419904,0.0002153255],"domain_scores_gemma":[0.99929,0.000288917,0.0001242888,0.00007485238,0.000006167664,0.0002157258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001767084,0.0003995537,0.002657241,0.00002785163,0.00004671172,0.00005243027,0.0008002903,0.001595295,0.9516188,0.0008576456,0.0001128111,0.04181368],"study_design_scores_gemma":[0.001938761,0.002229242,0.5219852,0.0005151407,0.0001357167,0.002437438,0.002643397,0.3557521,0.04034609,0.06974373,0.0008223415,0.001450791],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831393,0.0001499268,0.0163334,0.00008665459,0.00009218256,0.00007133268,0.000008660299,0.00001090838,0.0001076724],"genre_scores_gemma":[0.9661328,0.00004592488,0.03367377,0.00004890185,0.00003400701,0.000002413645,5.765694e-7,0.00001002272,0.00005159133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9112727,"threshold_uncertainty_score":0.4835008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009035131350435413,"score_gpt":0.2412939435634365,"score_spread":0.2322588122130011,"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."}}