{"id":"W2897468516","doi":"10.1002/fes3.151","title":"Adaptive Neuro‐Fuzzy Inference System integrated with solar zenith angle for forecasting sub‐tropical Photosynthetically Active Radiation","year":2018,"lang":"en","type":"article","venue":"Food and Energy Security","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"University of Southern Queensland","keywords":"Adaptive neuro fuzzy inference system; Mean squared error; Photosynthetically active radiation; Gene expression programming; Correlation coefficient; Coefficient of determination; Statistics; Mathematics; Meteorology; Computer science; Environmental science; Machine learning; Artificial intelligence; Fuzzy logic; Fuzzy control system; Physics; Botany; Photosynthesis; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004744885,0.0005754445,0.000440859,0.0004195363,0.0003433995,0.0005444356,0.0004654741,0.0005959101,0.0009203869],"category_scores_gemma":[0.0009576251,0.0002859503,0.0005615882,0.0003187921,0.0001437751,0.0002821333,0.0002451686,0.0005827983,0.0001629132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006184793,"about_ca_system_score_gemma":0.0006036372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03583923,"about_ca_topic_score_gemma":0.02742231,"domain_scores_codex":[0.9998654,0.00002533111,0.00001206495,0.00004281183,0.0000341785,0.00002017657],"domain_scores_gemma":[0.9997299,0.0001330519,0.00004083887,0.00001184496,0.00007152941,0.00001271984],"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":[0.0002021595,0.0001144454,0.005962566,0.00004728072,0.00006948279,0.00009406044,0.00005097898,0.9422556,0.003907657,0.0002889754,0.0006568387,0.04634992],"study_design_scores_gemma":[0.000002870457,0.00001130296,0.0005007689,0.000001784127,0.000005142631,0.00000165304,0.000003554727,0.9991692,0.0002276139,0.00003845738,0.00003584007,0.000001761419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.721517,0.0008013895,0.2688296,0.0003866731,0.0001644198,0.00008919134,0.0003804858,0.0009161364,0.006915087],"genre_scores_gemma":[0.9889898,0.00006378155,0.0101928,0.00001762016,0.000009711523,0.00002357054,0.00009168807,0.000005218699,0.0006057218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03583923,"threshold_uncertainty_score":0.07126123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0171863358707794,"score_gpt":0.2105447243531124,"score_spread":0.1933583884823331,"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."}}