{"id":"W2510703262","doi":"10.1002/joc.4881","title":"Association between three prominent climatic teleconnections and precipitation in Iran using wavelet coherence","year":2016,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Climate variability and models","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Teleconnection; Climatology; Precipitation; Coherence (philosophical gambling strategy); Wavelet; Environmental science; Meteorology; Geography; Geology; Mathematics; Statistics; Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007146935,0.00007575873,0.000191316,0.0001137018,0.00003243221,0.00002076009,0.0001672747,0.00008413291,0.000363251],"category_scores_gemma":[0.0005356474,0.00005704347,0.00004275358,0.00007065553,0.00006978644,0.0003977784,0.00007890635,0.0001060976,0.00001963914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000508929,"about_ca_system_score_gemma":0.00001679315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008483804,"about_ca_topic_score_gemma":0.0006737704,"domain_scores_codex":[0.9988016,0.0000956521,0.0005426296,0.0001296511,0.0002769814,0.0001534671],"domain_scores_gemma":[0.9988814,0.0005491435,0.0004064989,0.00005997178,0.00005482767,0.00004817207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003421765,0.00004682978,0.9895431,0.000004464566,0.00004519174,0.000006075509,0.0002305031,0.0002001747,0.006126377,0.0002641919,0.00003217664,0.003466639],"study_design_scores_gemma":[0.001578569,0.0001355184,0.9676288,0.0001316299,0.00005137484,0.000194726,0.00006322893,0.004399173,0.0007128598,0.02470264,0.0002659077,0.0001356007],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893204,0.000009058091,0.005769281,0.004215957,0.0002974577,0.0001039752,0.00001294848,0.000004987387,0.0002659133],"genre_scores_gemma":[0.9970432,0.00004478179,0.002775189,0.0000524037,0.00005624181,0.0000036414,0.000001606998,0.000005391988,0.00001756071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02443845,"threshold_uncertainty_score":0.3977344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03935948985126226,"score_gpt":0.2993447518908457,"score_spread":0.2599852620395834,"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."}}