{"id":"W1647484032","doi":"10.1029/2002gl015836","title":"Detecting anthropogenic influence with a multi‐model ensemble","year":2002,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Climate variability and models","field":"Environmental Science","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pacific Institute for Climate Solutions; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Department for Environment, Food and Rural Affairs, UK Government; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Aerosol; Environmental science; Greenhouse gas; Scaling; Consistency (knowledge bases); Atmospheric sciences; Climatology; Meteorology; Mathematics; Geology; Physics","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":[0.0004375485,0.0001416681,0.000144726,0.00004677228,0.0004168276,0.00006807313,0.0003517077,0.00004877564,0.0005014599],"category_scores_gemma":[0.0001948968,0.000114913,0.00005739037,0.0005143132,0.0008838414,0.000345508,0.000350644,0.0005361232,0.001928603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002007782,"about_ca_system_score_gemma":0.000007935649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001106751,"about_ca_topic_score_gemma":0.0001466902,"domain_scores_codex":[0.9974463,0.0001428555,0.0001486818,0.000535085,0.000879796,0.0008472552],"domain_scores_gemma":[0.9989938,0.0002853011,0.00002803363,0.000454421,0.00002291475,0.000215573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004977639,0.000359604,0.005182551,0.00001997701,0.0000131496,0.00004193845,0.001123403,0.0861735,0.9014012,0.00008394018,0.0008725327,0.004678469],"study_design_scores_gemma":[0.0007508587,0.0002297126,0.01415777,0.00003579017,0.000009669377,0.000009267828,0.0000936925,0.9748911,0.008207149,0.0007572174,0.000476994,0.0003807395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992814,0.000006205465,0.00386969,0.001996137,0.00001135238,0.0002640536,0.000003141384,0.0000557839,0.0009796085],"genre_scores_gemma":[0.9953452,0.00001106946,0.003567655,0.0007077982,0.00003035132,0.00004974586,8.462984e-7,0.00001962645,0.0002677231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.893194,"threshold_uncertainty_score":0.9988485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06858393138657572,"score_gpt":0.3178076818658301,"score_spread":0.2492237504792544,"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."}}