{"id":"W4321196325","doi":"10.1016/j.solener.2023.02.013","title":"Monte-Carlo estimation of geometric sensitivities in Solar Power Tower systems of flat mirrors","year":2023,"lang":"en","type":"article","venue":"Solar Energy","topic":"Solar Thermal and Photovoltaic Systems","field":"Energy","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Agence Nationale de la Recherche","keywords":"Heliostat; Monte Carlo method; Sensitivity (control systems); Tower; Computer science; Intensity (physics); Power (physics); Geometrical optics; Blocking (statistics); Geometric shape; Physics; Solar energy; Optics; Geometry; Mathematics; Electronic engineering; Statistics; Electrical engineering","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":[0.0009801534,0.0004371565,0.0005020828,0.0005083004,0.0004401995,0.0007497947,0.000632841,0.0008153154,0.001312854],"category_scores_gemma":[0.00506578,0.0007374236,0.0005745706,0.0005907062,0.0004721111,0.0006543654,0.0005403957,0.0004942217,0.0001727657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001244345,"about_ca_system_score_gemma":0.0007146094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01665466,"about_ca_topic_score_gemma":0.01483724,"domain_scores_codex":[0.9995824,0.0001762959,0.00001559052,0.0000737298,0.00008615769,0.00006592283],"domain_scores_gemma":[0.9961702,0.00309449,0.0001871927,0.0001478313,0.0003128088,0.00008755042],"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":[0.00004246351,0.000009344662,0.001774083,0.000008871072,0.00001295953,0.00002040077,0.000008744807,0.9956194,0.0004987911,0.0006334017,0.00005591968,0.001315691],"study_design_scores_gemma":[0.000003168058,0.000006769773,0.001099544,0.000001598059,0.000003185138,0.000008162775,0.000004942001,0.998202,0.0003080809,0.0003337844,0.0000244222,0.000004360361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8165644,0.0003392592,0.1747444,0.0001677942,0.00002701599,0.00004740957,0.0002435812,0.0003930212,0.007473023],"genre_scores_gemma":[0.994855,0.00002305785,0.004658404,0.00001184075,0.000003787618,0.000007035694,0.00006720344,0.00001747966,0.0003561747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01665466,"threshold_uncertainty_score":0.03311545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02100590473713422,"score_gpt":0.2361462027660529,"score_spread":0.2151402980289187,"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."}}