{"id":"W3026390873","doi":"10.1145/3388538","title":"Delayed Rejection Metropolis Light Transport","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Japan Society for the Promotion of Science; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Markov chain; Benchmark (surveying); Ergodicity; Kernel (algebra); State space; Markov chain Monte Carlo; Sample (material); Algorithm; Sample space; Mathematical optimization; Artificial intelligence; Mathematics; Machine learning; Bayesian probability","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":[0.0001535061,0.0002171628,0.0002010988,0.0004329577,0.00032382,0.0001014846,0.001096967,0.0001387159,0.0000295496],"category_scores_gemma":[0.00001293378,0.0002203157,0.0002543337,0.002532576,0.00004389421,0.000467425,0.00001376622,0.0003554024,0.00001615869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000022858,"about_ca_system_score_gemma":0.00004940671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005247827,"about_ca_topic_score_gemma":0.00003749717,"domain_scores_codex":[0.9984263,0.00006695367,0.0003423984,0.0005319123,0.0003839671,0.0002485206],"domain_scores_gemma":[0.99868,0.00006275167,0.00008415031,0.0008084381,0.0001490094,0.0002156312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007097248,0.0004895871,0.000354278,0.0000494642,0.0002278822,0.00002902144,0.002464154,0.0001930152,0.0008674394,0.95568,0.002724955,0.03684925],"study_design_scores_gemma":[0.002899869,0.005307066,0.004521876,0.0001282647,0.0002930682,0.00008737551,0.0001912641,0.4647889,0.1377933,0.1510762,0.2300049,0.002907887],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001762174,0.00004601803,0.989072,0.007135344,0.0002908439,0.000197833,0.0000124571,0.001219744,0.0002636354],"genre_scores_gemma":[0.9716781,0.0003935956,0.02288381,0.004886532,0.00006266988,0.00003634583,0.000008676666,0.00002579956,0.00002447559],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9699159,"threshold_uncertainty_score":0.8984214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02806391939420383,"score_gpt":0.2759656322744736,"score_spread":0.2479017128802698,"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."}}