{"id":"W3097044656","doi":"10.5194/hess-2020-315","title":"Technical Note: Partial wavelet coherency for improved understanding of scale-specific and localized bivariate relationships in geosciences","year":2020,"lang":"en","type":"article","venue":"","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"New Zealand Institute for Plant and Food Research Limited","keywords":"Bivariate analysis; Wavelet; Scale (ratio); Variable (mathematics); Multivariate statistics; Statistics; Variables; Bivariate data; Computer science; Econometrics; Mathematics; Data mining; Artificial intelligence; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003880672,0.001120167,0.0004969349,0.001386339,0.0004038477,0.001235486,0.00135428,0.0008177672,0.01267092],"category_scores_gemma":[0.01783243,0.0005089413,0.001065043,0.002434918,0.000850078,0.001966954,0.00161293,0.002132842,0.005260122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003603885,"about_ca_system_score_gemma":0.001339437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002258579,"about_ca_topic_score_gemma":0.002159055,"domain_scores_codex":[0.9989225,0.0003172756,0.00009373658,0.0001887828,0.000430086,0.00004771364],"domain_scores_gemma":[0.9938244,0.002574756,0.0003987972,0.0012423,0.001796445,0.0001634258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001976537,0.0001015389,0.003361778,0.001003765,0.0001324692,0.0009209365,0.0003484836,0.03942159,0.06812644,0.126554,0.07437456,0.6854569],"study_design_scores_gemma":[0.00006946538,0.0001325077,0.003950492,0.0002053045,0.00009146497,0.001241354,0.00009872213,0.7248557,0.04116927,0.07296142,0.1550433,0.0001811094],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001678671,0.0002492341,0.9950945,0.0004364982,0.0003236993,0.00004810796,0.0002779136,0.000818303,0.001073029],"genre_scores_gemma":[0.02482432,0.0009932555,0.9693831,0.0002094636,0.0003968622,0.0002054187,0.0008313945,0.0005774706,0.002578646],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01267092,"threshold_uncertainty_score":0.04238838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06243133746544277,"score_gpt":0.327137170350085,"score_spread":0.2647058328846422,"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."}}