{"id":"W2049031091","doi":"10.1002/asl.290","title":"Panel and multivariate methods for tests of trend equivalence in climate data series","year":2010,"lang":"en","type":"article","venue":"Atmospheric Science Letters","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Multivariate statistics; Econometrics; Climatology; Covariance; Equivalence (formal languages); Mathematics; Series (stratigraphy); Panel data; Multivariate analysis; Statistics; Environmental science; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002025359,0.0001097945,0.0003042224,0.00003091305,0.000108906,0.00007599748,0.000829099,0.00003907497,0.00005755706],"category_scores_gemma":[0.0006291472,0.0001088089,0.00003165573,0.0008414863,0.0005370401,0.00095376,0.0003296636,0.00009695999,0.000005879078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001311365,"about_ca_system_score_gemma":0.00001459879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001482218,"about_ca_topic_score_gemma":0.0004062306,"domain_scores_codex":[0.9985936,0.00001316349,0.0004133425,0.0005956077,0.00004278725,0.0003415114],"domain_scores_gemma":[0.998789,0.0001914507,0.0002503209,0.0006890345,0.00001412544,0.00006609088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005448079,0.00009973589,0.6289638,0.0001053535,0.00003695309,0.000003926777,0.001280582,0.0002123305,0.2653157,0.02022848,0.0001645927,0.08353412],"study_design_scores_gemma":[0.0008023551,0.00009223846,0.8113118,0.00002463312,0.00002552575,0.000009820781,0.0001934064,0.1729546,0.00163535,0.004580136,0.007866576,0.0005035208],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9740523,0.0001860326,0.02308903,0.001549116,0.0004108161,0.0001591013,0.0003360653,0.00001421851,0.0002033505],"genre_scores_gemma":[0.6487562,0.00008651338,0.3506771,0.0003877223,0.00003166351,0.00001291227,0.00002295084,0.00000761151,0.00001729568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3275881,"threshold_uncertainty_score":0.4437099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09453405304447306,"score_gpt":0.3375180834345921,"score_spread":0.2429840303901191,"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."}}