{"id":"W3186002470","doi":"10.1088/1748-9326/ac14ee","title":"Uncertainty in optimal fingerprinting is underestimated","year":2021,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Climate variability and models","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"National Science Foundation","keywords":"Statistics; Econometrics; Scaling; Confidence interval; Forcing (mathematics); Environmental science; Sample size determination; Calibration; Climate change; Climate model; Sample (material); Omitted-variable bias; Computer science; Climatology; Mathematics; Ecology","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":[0.01312419,0.0007782262,0.00128394,0.002848312,0.0008133766,0.002119571,0.001288298,0.001223024,0.001009645],"category_scores_gemma":[0.07937073,0.0005550872,0.0009031382,0.001823326,0.002136615,0.003281894,0.002173952,0.001439588,0.0002273804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007880008,"about_ca_system_score_gemma":0.000857855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002570117,"about_ca_topic_score_gemma":0.001012789,"domain_scores_codex":[0.9894084,0.004257779,0.0005862751,0.00268922,0.002445835,0.0006124261],"domain_scores_gemma":[0.9353002,0.04639876,0.005222516,0.009268425,0.003348386,0.0004617563],"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":[0.0009392966,0.0001584243,0.0837552,0.0005448696,0.0005016392,0.0006391981,0.0009005855,0.5038613,0.02139745,0.05144191,0.002081513,0.3337785],"study_design_scores_gemma":[0.00003313972,0.000119989,0.02146764,0.00019498,0.0001064464,0.0005606789,0.0002453615,0.8620971,0.02288044,0.08989111,0.002259045,0.0001440851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1488641,0.0007934934,0.8468986,0.0004145983,0.00007427821,0.00002948487,0.0001700988,0.0005361406,0.002219257],"genre_scores_gemma":[0.9075925,0.0001926521,0.09164934,0.00009297481,0.00005335775,0.00002799918,0.0001361077,0.00007649052,0.0001785143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01312419,"threshold_uncertainty_score":0.06940812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05443228819593719,"score_gpt":0.3208279395218018,"score_spread":0.2663956513258646,"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."}}