{"id":"W2906232045","doi":"","title":"Understanding Blur and Model Learning in Projector Compensation","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Projector; Computer science; Computer vision; Artificial intelligence; Point (geometry); Compensation (psychology); Perception; Computer graphics (images); Mathematics","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":[0.001133263,0.0005075021,0.0005899494,0.0003380366,0.0002978545,0.001048532,0.0006681854,0.00106167,0.0007979407],"category_scores_gemma":[0.006980871,0.0004245708,0.0003857533,0.0003742024,0.0008347259,0.002857184,0.001210946,0.001300375,0.0001076222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000616931,"about_ca_system_score_gemma":0.0005332645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004922204,"about_ca_topic_score_gemma":0.003084792,"domain_scores_codex":[0.9996573,0.000132525,0.00001680107,0.00006116425,0.00008709416,0.00004513945],"domain_scores_gemma":[0.997937,0.001372894,0.0001802803,0.0002269714,0.0002238888,0.00005908691],"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":[0.0001956487,0.0001109969,0.001434359,0.00013506,0.00005769349,0.0001073494,0.0003204872,0.7933092,0.01720041,0.03691031,0.0008680793,0.1493504],"study_design_scores_gemma":[0.000003465258,0.00001920804,0.0002010222,0.000002751187,0.000003939751,0.00001503019,0.00001176706,0.9914482,0.001524672,0.006613727,0.0001494541,0.000006853985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04978562,0.0007189244,0.948277,0.0004316384,0.00002945508,0.00001268135,0.00001524653,0.0001273583,0.0006020869],"genre_scores_gemma":[0.8639765,0.001264835,0.1329542,0.0001367328,0.00008782635,0.0000348891,0.00004279343,0.00005254143,0.001449787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004922204,"threshold_uncertainty_score":0.009787142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04481814381841916,"score_gpt":0.3104162512863905,"score_spread":0.2655981074679714,"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."}}