{"id":"W3036883119","doi":"10.1111/cgf.14051","title":"Practical Product Path Guiding Using Linearly Transformed Cosines","year":2020,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Precomputation; Path tracing; Radiance; Ray tracing (physics); Global illumination; Path (computing); Sampling (signal processing); Product (mathematics); Algorithm; Preprocessor; Computer vision; Rendering (computer graphics); Computation; Artificial intelligence; Mathematics; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001974785,0.0002405091,0.0002641357,0.000131189,0.0002936829,0.0003413551,0.0006793569,0.00005186738,0.000003640018],"category_scores_gemma":[0.000075915,0.0002200885,0.0001439405,0.0009830797,0.00008000905,0.001505644,0.0003656262,0.0002580763,0.00001667171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001980431,"about_ca_system_score_gemma":0.0001111615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004328295,"about_ca_topic_score_gemma":6.757766e-7,"domain_scores_codex":[0.9979934,0.00006406133,0.0003718804,0.0006671071,0.0003768044,0.0005267391],"domain_scores_gemma":[0.998899,0.00008789637,0.0001142602,0.0004405859,0.0001828082,0.0002754535],"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":[0.00009078862,0.0004655059,0.005660556,0.000286547,0.0001701954,0.0004598237,0.004184406,0.001143512,0.01370094,0.5837514,0.03687532,0.353211],"study_design_scores_gemma":[0.0003823161,0.0001300765,0.0001578672,0.00005219139,0.000008778156,0.00007762205,0.00001936366,0.9664859,0.001945688,0.001810151,0.02863123,0.0002988629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001566696,0.0001723557,0.9711973,0.0256778,0.0006872436,0.000232822,0.000001998475,0.0003893194,0.00007447221],"genre_scores_gemma":[0.2695677,0.00004935861,0.7212315,0.008721125,0.0003918674,0.000003911863,0.000003607478,0.00002583856,0.000005105594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9653423,"threshold_uncertainty_score":0.8974949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09495381207704867,"score_gpt":0.3278142397877524,"score_spread":0.2328604277107037,"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."}}