{"id":"W2296250040","doi":"10.1109/icmla.2015.87","title":"Prediction of SPEI Using MLR and ANN: A Case Study for Wilsons Promontory Station in Victoria","year":2015,"lang":"en","type":"article","venue":"","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"University of Southern Queensland","keywords":"Promontory; Mean squared error; Evapotranspiration; Artificial neural network; Linear regression; Statistics; Regression; Coefficient of determination; Precipitation; Mathematics; Computer science; Artificial intelligence; Meteorology; Geography; Ecology; Cartography","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":[0.0004957669,0.00005590461,0.00008390655,0.00002543309,0.00003553663,0.000008201305,0.0000292784,0.00003348882,0.00002208818],"category_scores_gemma":[0.0001134078,0.00004633797,0.000008397999,0.0001100876,0.00005701463,0.0001466488,0.00004476398,0.00003660526,0.00000140973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009788181,"about_ca_system_score_gemma":0.000009153276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003497478,"about_ca_topic_score_gemma":0.002136041,"domain_scores_codex":[0.9994037,0.00004822885,0.0001543859,0.0001635421,0.000124425,0.0001057085],"domain_scores_gemma":[0.9997788,0.00003171827,0.00004913914,0.0000720949,0.000009555742,0.00005869449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008838044,0.0006204977,0.9468818,0.00001375846,0.00000826219,0.0001067539,0.01162451,0.0315252,0.006688792,0.00001127842,0.0002692604,0.002161464],"study_design_scores_gemma":[0.00289622,0.002824891,0.08228261,0.00002423581,0.00004822627,0.0002829702,0.004239589,0.9051307,0.0005592292,0.001039757,0.000460136,0.0002114806],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976596,0.000003956282,0.001336816,0.00001885084,0.00007561368,0.0005164015,0.000007695439,0.000020504,0.0003605563],"genre_scores_gemma":[0.994659,1.397054e-7,0.005255631,0.00001017025,0.00001664987,0.00001136242,0.000001467762,0.000004690761,0.0000408668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8736054,"threshold_uncertainty_score":0.5287164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1329588816235091,"score_gpt":0.3115383113126274,"score_spread":0.1785794296891183,"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."}}