{"id":"W2077853268","doi":"10.3354/cr030175","title":"Analysis of consecutive droughts on the Canadian Prairies","year":2006,"lang":"en","type":"article","venue":"Climate Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Geography; Precipitation; Agriculture; Percentile; Growing season; Forestry; Demography; Meteorology; Agronomy; Mathematics; Statistics; Biology; Archaeology","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":["insufficient_payload"],"category_scores_codex":[0.001668119,0.00007198576,0.0001787013,0.0003118498,0.000692663,0.00002867625,0.0002997686,0.00008042993,0.003295482],"category_scores_gemma":[0.0001198899,0.00004673708,0.0001079591,0.002275398,0.00139046,0.0000505713,0.0001045439,0.0002391756,0.0009061226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001942984,"about_ca_system_score_gemma":0.00004848113,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4005601,"about_ca_topic_score_gemma":0.9066269,"domain_scores_codex":[0.998244,0.0003707279,0.0001627089,0.0002297772,0.0004876398,0.0005051477],"domain_scores_gemma":[0.9989278,0.000559799,0.00003905327,0.0003535467,0.00003322422,0.00008662006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003761447,0.00008549119,0.9501134,0.000003553221,0.0003022042,0.00003524348,0.000481907,0.004715385,0.0003192152,0.03097816,0.01271419,0.0002136912],"study_design_scores_gemma":[0.0001351382,0.0001121145,0.9580188,0.000007099908,0.0002905738,0.000001544475,0.0003836101,0.01208958,0.002036538,0.006265047,0.02049739,0.0001626026],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7595511,0.0000195308,0.000002857234,0.003657598,0.000008964104,0.00009139563,0.00002934121,0.00000594634,0.2366332],"genre_scores_gemma":[0.9986768,0.00001689101,0.00001354415,0.0001256852,0.00001294265,0.00001538199,0.00002258361,0.000004515537,0.001111635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5060669,"threshold_uncertainty_score":0.9998718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04190512944737091,"score_gpt":0.3322496550399515,"score_spread":0.2903445255925806,"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."}}