{"id":"W2020315078","doi":"10.2495/sdp-v10-n1-29-41","title":"Using lagged and forecast climate indices with artificial intelligence to predict monthly rainfall in the brisbane catchment, Queensland, Australia","year":2015,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"B. Macfie Family Foundation","keywords":"Climatology; Environmental science; El Niño Southern Oscillation; Streamflow; Meteorology; Drainage basin; Index (typography); Forecast skill; Geography; Geology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005812875,0.0003258444,0.0002585854,0.0004800128,0.0002622363,0.0006867664,0.0004307365,0.0003178623,0.0006208071],"category_scores_gemma":[0.002095205,0.0002454036,0.0002468313,0.0007522423,0.0002233435,0.0005540808,0.0003629835,0.0004498096,0.0001169635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001640787,"about_ca_system_score_gemma":0.001140537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2322793,"about_ca_topic_score_gemma":0.2492814,"domain_scores_codex":[0.9998335,0.00005555243,0.00001495944,0.00003791546,0.00003807271,0.00001996492],"domain_scores_gemma":[0.9996284,0.0001367847,0.00006395936,0.00002480661,0.0001141328,0.0000317861],"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":[0.000191153,0.0003000721,0.1867758,0.0001020152,0.0001809947,0.0002788663,0.0004578091,0.7689736,0.004660436,0.0007540614,0.001191528,0.03613376],"study_design_scores_gemma":[0.00001960961,0.00005891354,0.07101098,0.0000120452,0.00002398593,0.00001132232,0.0001152599,0.9275078,0.0005489358,0.0002902075,0.000384778,0.00001619756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951211,0.0000992135,0.00266757,0.0001622498,0.0000127806,0.00001879241,0.0002530856,0.00008182417,0.001583409],"genre_scores_gemma":[0.9962913,0.00006329394,0.002852675,0.00001241852,0.000004386177,0.00001136134,0.0002663743,0.000004991208,0.0004932657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2322793,"threshold_uncertainty_score":0.4618545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08057802488843593,"score_gpt":0.313067027670807,"score_spread":0.2324890027823711,"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."}}