{"id":"W2038011122","doi":"10.1115/ihtc14-23389","title":"Geometrical Optimization of Solar Concentrating Collectors Through Quasi-Monte Carlo Simulation","year":2010,"lang":"en","type":"article","venue":"","topic":"Radiative Heat Transfer Studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Monte Carlo method; Computer science; Mathematical optimization; Quasi-Monte Carlo method; Logarithm; Algorithm; Hybrid Monte Carlo; Mathematics; Markov chain Monte Carlo; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00007155905,0.0001139593,0.0001905484,0.00007832465,0.00006518551,0.00001456899,0.00005928306,0.00007751793,0.0000906086],"category_scores_gemma":[0.0001372553,0.0001085472,0.00004468375,0.0005381628,0.00004212049,0.000189638,0.000007012547,0.0001450953,0.000002719174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003668714,"about_ca_system_score_gemma":0.00001281302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006006229,"about_ca_topic_score_gemma":0.00007173514,"domain_scores_codex":[0.9992995,0.00001596982,0.0002536405,0.0001126143,0.0001513323,0.0001669563],"domain_scores_gemma":[0.9995021,0.0002481536,0.00001609512,0.0001056882,0.00009549664,0.00003251241],"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":[0.00000331684,0.00001866914,0.006625764,0.00002571871,0.00004154219,3.982619e-7,0.0007495641,0.9903362,0.001619839,0.0001961014,0.00007039136,0.0003125094],"study_design_scores_gemma":[0.0002995218,0.00004435795,0.003660722,0.000006053705,0.00001633114,3.460916e-7,0.000102198,0.9873434,0.008165118,0.000008525096,0.0002363685,0.0001170869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4446359,0.000225219,0.5484731,0.000027939,0.000446046,0.000253678,0.00001012741,0.000233085,0.005694942],"genre_scores_gemma":[0.9883711,0.00005580563,0.01145677,0.0000107561,0.00005148723,0.000007875024,0.000002104551,0.00002086098,0.00002325003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5437352,"threshold_uncertainty_score":0.4426427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0150372105722852,"score_gpt":0.2479450792921774,"score_spread":0.2329078687198921,"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."}}