{"id":"W2980802779","doi":"10.1016/j.atmosres.2019.104695","title":"Spatiotemporal changes of drought characteristics and their dynamic drivers in Canada","year":2019,"lang":"en","type":"article","venue":"Atmospheric Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":82,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Pacific decadal oscillation; Climatology; Teleconnection; Environmental science; Cru; Precipitation; El Niño Southern Oscillation; Evapotranspiration; Multivariate ENSO index; North Atlantic oscillation; Climate change; Atlantic multidecadal oscillation; Indian Ocean Dipole; Southern oscillation; Arctic oscillation; Global warming; Geography; Oceanography; Meteorology; Ecology; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004263143,0.00007408963,0.0001801878,0.000005650832,0.00004331059,0.000005969612,0.0001795751,0.00004876334,0.001748528],"category_scores_gemma":[0.00002755955,0.00006079365,0.00001452477,0.0004801749,0.0001811333,0.00006593627,0.0001724633,0.0001875045,0.00006116113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003058546,"about_ca_system_score_gemma":0.0001043174,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7360961,"about_ca_topic_score_gemma":0.9166046,"domain_scores_codex":[0.9989685,0.0001295005,0.0001291372,0.0002194065,0.0002676212,0.0002858544],"domain_scores_gemma":[0.9995524,0.0001282239,0.00004254829,0.0002069028,0.00001086347,0.0000590183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001476858,0.00001504785,0.989674,0.00001003426,0.000009721036,0.000009657952,0.0004526717,0.0001846031,0.001384665,0.00001215603,0.0001011153,0.008131533],"study_design_scores_gemma":[0.0001921711,0.00007349068,0.9248644,0.000008795308,0.000002416935,0.000001569551,0.0005371616,0.07001089,0.0002594839,0.0001532639,0.003804416,0.00009197272],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966433,0.00005275351,0.0000156277,0.0004962423,0.00003340125,0.0001285001,0.000004033845,0.000002765858,0.002623359],"genre_scores_gemma":[0.998554,0.0001358489,0.0001883335,0.00004692757,0.000004824871,0.000005874766,0.00000574653,0.00000618699,0.001052272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1805085,"threshold_uncertainty_score":0.999164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01024951624229809,"score_gpt":0.2467410601439473,"score_spread":0.2364915439016493,"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."}}