{"id":"W4253107628","doi":"10.5194/cpd-4-213-2008","title":"East Asian Monsoon and paleoclimatic data analysis: a vegetation point of view","year":2008,"lang":"en","type":"article","venue":"","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"GDG Environnement; Université du Québec à Montréal","funders":"Agence Nationale de la Recherche; European Science Foundation","keywords":"Monsoon; East Asian Monsoon; Holocene; Climatology; China; Precipitation; Physical geography; Climate change; Context (archaeology); Last Glacial Maximum; Arid; Tropical monsoon climate; Glacial period; Geology; Geography; Environmental science; Oceanography; Meteorology; Geomorphology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002291548,0.0004535494,0.0003352229,0.003436423,0.0002829088,0.002846549,0.0005114858,0.0002531383,0.003949918],"category_scores_gemma":[0.003076646,0.0001814062,0.0004280939,0.008827987,0.0004244456,0.001503617,0.001037802,0.0004457565,0.0008271873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005149902,"about_ca_system_score_gemma":0.001441527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004458781,"about_ca_topic_score_gemma":0.004501428,"domain_scores_codex":[0.9992633,0.0002389726,0.0001769542,0.0001308813,0.0001450153,0.00004492252],"domain_scores_gemma":[0.9980406,0.0004672476,0.000283353,0.0005071422,0.0005761336,0.0001254926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002012951,0.00006742855,0.2306159,0.002200824,0.001093491,0.00128288,0.001554599,0.003667882,0.008351445,0.02726103,0.02633093,0.6973724],"study_design_scores_gemma":[0.00002952979,0.0000933987,0.3974945,0.001063209,0.00046911,0.002144453,0.003791023,0.02305134,0.00937341,0.02535194,0.5370685,0.00006954479],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3509463,0.1569649,0.3260898,0.01424523,0.002392685,0.0002969619,0.0642457,0.006109254,0.07870914],"genre_scores_gemma":[0.7919983,0.04149979,0.1190756,0.001127653,0.001670676,0.0002675803,0.02860656,0.001130119,0.01462368],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004458781,"threshold_uncertainty_score":0.01321375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05915920953385326,"score_gpt":0.2675015099062374,"score_spread":0.2083423003723842,"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."}}