{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004563221,0.00007361796,0.0001401848,0.0002867205,0.0001192093,0.0002262621,0.00009296331,0.00001574597,0.000001020306],"category_scores_gemma":[0.00004114446,0.00006047087,0.00002334086,0.0001234896,0.00004691575,0.0008924578,0.00004699949,0.0001342498,0.000001700541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007834189,"about_ca_system_score_gemma":0.00006285367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007821475,"about_ca_topic_score_gemma":6.061489e-7,"domain_scores_codex":[0.9991865,0.0000904136,0.0002931969,0.0001038044,0.0002311572,0.00009494528],"domain_scores_gemma":[0.9992382,0.0001428802,0.000250431,0.00003502595,0.0002889107,0.00004457616],"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":[0.0003371683,0.0002900637,0.05819415,0.0002175771,0.0001415516,0.00009508261,0.03389281,0.5070078,0.0373816,0.3458637,0.005383008,0.01119551],"study_design_scores_gemma":[0.0004739586,0.0001201919,0.01321017,0.0002033106,0.00000282358,0.0002171012,0.0009919876,0.9809846,0.00003170501,0.003593271,0.0001026283,0.0000682293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1183264,0.0001547777,0.8802199,0.0005694974,0.0002494994,0.00005699701,3.231789e-7,0.000004332604,0.0004183158],"genre_scores_gemma":[0.9959584,0.000009699209,0.00382286,0.0001117906,0.00006811562,3.895371e-7,6.492867e-7,0.000003946469,0.00002415404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.877632,"threshold_uncertainty_score":0.246593,"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."}}