{"id":"W2064685841","doi":"10.1029/2009jd013622","title":"Estimates of past and future ozone trends from multimodel simulations using a flexible smoothing spline methodology","year":2010,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Natural Environment Research Council; Sight Research UK; National Oceanic and Atmospheric Administration; Scheme for Promotion of Academic and Research Collaboration; Canadian Foundation for Climate and Atmospheric Sciences; University Corporation for Atmospheric Research","keywords":"Smoothing; Inference; Series (stratigraphy); Baseline (sea); Computer science; Probabilistic logic; Econometrics; Smoothing spline; Trend analysis; Environmental science; Statistics; Mathematics; Artificial intelligence; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002008904,0.0004930527,0.0005819142,0.001181859,0.0003670519,0.0007689356,0.0008273701,0.0004922246,0.001513152],"category_scores_gemma":[0.006371698,0.0003420729,0.00138204,0.001203218,0.0001755714,0.0008043909,0.0008716302,0.001030713,0.0002003735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005743174,"about_ca_system_score_gemma":0.0009399219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008490339,"about_ca_topic_score_gemma":0.00787725,"domain_scores_codex":[0.9995146,0.0001928296,0.00002793407,0.00009170931,0.0001382676,0.00003475304],"domain_scores_gemma":[0.9983618,0.001036332,0.0002099853,0.000181108,0.0001737804,0.00003703769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000224769,0.00003044049,0.002559593,0.00004203661,0.0001212586,0.00003427344,0.00004389346,0.9349297,0.001128575,0.01305938,0.0003692443,0.0476592],"study_design_scores_gemma":[0.000002288742,0.0000119821,0.0005112205,0.000004584091,0.00001038422,0.000007626535,0.000005085443,0.9942967,0.0001953459,0.004531232,0.0004164739,0.000006984893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02746435,0.0000577802,0.971256,0.00006312023,0.00001837366,0.00001946283,0.000173477,0.0002479862,0.000699496],"genre_scores_gemma":[0.5713917,0.0002833776,0.4247065,0.00004449348,0.00007111287,0.000207169,0.0009938354,0.0002213887,0.002080521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008490339,"threshold_uncertainty_score":0.01688182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09014283485449795,"score_gpt":0.3714470627627352,"score_spread":0.2813042279082373,"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."}}