{"id":"W2765245069","doi":"10.5539/mas.v11n11p66","title":"PV Improved Power Using Off-Normal Sun Tracking","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Jordan","keywords":"Photovoltaic system; Tracking (education); Power (physics); Environmental science; Orientation (vector space); Maximum power point tracking; Computer science; Normality; Wind speed; Work (physics); Solar tracker; Meteorology; Automotive engineering; Control theory (sociology); Mathematics; Physics; Electrical engineering; Artificial intelligence; Statistics; Engineering; Geometry","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":[0.00008694829,0.0001594398,0.0002112896,0.0001235534,0.0001280283,0.0003446607,0.0002324044,0.0001406659,0.00120392],"category_scores_gemma":[0.0001146005,0.00006152486,0.0002214647,0.00029799,0.0001217246,0.0002988261,0.0001773757,0.0001309841,0.0002223964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001711267,"about_ca_system_score_gemma":0.0001073061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002614778,"about_ca_topic_score_gemma":0.0004978105,"domain_scores_codex":[0.9999486,0.000006611358,0.000001526699,0.00001568471,0.00002021118,0.000007261917],"domain_scores_gemma":[0.9999574,0.0000108248,0.000006619668,0.000008986673,0.00001407403,0.000002005444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005790747,0.0001571724,0.009460804,0.0002998809,0.0000807287,0.0004119196,0.0002335005,0.278941,0.4880789,0.006349759,0.001640635,0.2137666],"study_design_scores_gemma":[0.00002275528,0.0003700678,0.01120795,0.00001860939,0.00004019867,0.0002455994,0.00005968053,0.8403634,0.1395022,0.002038513,0.006115811,0.00001527942],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8221304,0.0003660652,0.1561445,0.00007639098,0.00004677108,0.00003612883,0.0001893795,0.0005676752,0.02044258],"genre_scores_gemma":[0.9953216,0.0000653051,0.003203368,0.000005366553,0.000004068173,0.000005260898,0.00004174769,0.00001416529,0.001339165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00120392,"threshold_uncertainty_score":0.004027545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03276810409730308,"score_gpt":0.2800334533070304,"score_spread":0.2472653492097274,"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."}}