{"id":"W4312765600","doi":"10.1017/sus.2022.17","title":"Ten new insights in climate science 2022","year":2022,"lang":"en","type":"article","venue":"Global Sustainability","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Future Earth; Natural Resources Canada; Canadian Forest Service; Université du Québec à Montréal","funders":"Canadian Forest Service; Natural Environment Research Council; U.S. Forest Service; National Institute of Food and Agriculture; Instituto Serrapilheira; Bundesministerium für Bildung und Forschung; Akademie der Naturwissenschaften; Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit; Max-Planck-Gesellschaft; Svenska Forskningsrådet Formas; Norges Forskningsråd; University of Cambridge; European Commission; Sight Research UK; European Space Agency; U.S. Department of Agriculture","keywords":"Climate science; Environmental science; Earth science; Climate change; Geology; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001222231,0.0001287393,0.0001572865,0.00005037737,0.0007100774,0.00003839677,0.0005323305,0.0000295543,0.003954366],"category_scores_gemma":[0.0003076802,0.0001262902,0.00003755042,0.002166758,0.0003586903,0.0003598717,0.001893951,0.0001991822,0.00008095756],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009821003,"about_ca_system_score_gemma":0.0003884748,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007804785,"about_ca_topic_score_gemma":0.001879198,"domain_scores_codex":[0.9974129,0.000109383,0.0002625728,0.0005591201,0.0006896995,0.0009663815],"domain_scores_gemma":[0.9990559,0.0000252637,0.00006794136,0.0004386074,0.00001946208,0.0003927907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009850291,0.0002472496,0.9604753,0.0000338235,5.827477e-7,0.00006269709,0.001667445,0.001320983,0.000056028,0.00192024,0.006095449,0.02802171],"study_design_scores_gemma":[0.0003810522,0.0001507999,0.9101717,0.000002223328,0.000002437072,0.00001132494,0.002823408,0.0003142141,0.00001471933,0.04033623,0.04562274,0.0001691108],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786554,0.0001216729,0.000005556936,0.004405668,0.0001812498,0.0005328758,0.00002721088,0.00004812248,0.01602224],"genre_scores_gemma":[0.998402,0.0000340208,0.00005633746,0.00130219,0.0000263354,0.00004373336,0.000005801936,0.000004825617,0.0001247838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05030356,"threshold_uncertainty_score":0.9988023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01766855057910286,"score_gpt":0.3112669315959964,"score_spread":0.2935983810168936,"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."}}