{"id":"W2016433393","doi":"10.1109/tcsvt.2013.2291281","title":"Visual Comfort Amelioration Technique for Stereoscopic Images: Disparity Remapping to Mitigate Global and Local Discomfort Causes","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Advanced Optical Imaging Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Naturalness; Stereoscopy; Computer vision; Artificial intelligence; Process (computing); Stereopsis; Visualization; Range (aeronautics); Binocular disparity","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001101404,0.000282885,0.0004255752,0.0003110317,0.0002297092,0.0001088795,0.0001591736,0.0002964677,9.763141e-7],"category_scores_gemma":[0.00002516487,0.0002732653,0.0000535315,0.0003462644,0.0002890092,0.0003150721,0.000006573692,0.0002274016,0.000002449819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001619857,"about_ca_system_score_gemma":0.00001496135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007228284,"about_ca_topic_score_gemma":0.00008658846,"domain_scores_codex":[0.9986178,0.00001079631,0.0004106705,0.0004169132,0.00009668603,0.000447104],"domain_scores_gemma":[0.9993823,0.00009368847,0.00005176932,0.0002937955,0.00007698483,0.000101454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000777459,0.0003655461,0.002081687,0.005128046,0.0006522593,0.00001512193,0.0002047335,0.04397768,0.451667,0.02761288,0.001135583,0.4670817],"study_design_scores_gemma":[0.003408138,0.002716654,0.0007260841,0.002351571,0.0003558259,0.0005220466,0.003790566,0.2406612,0.6945832,0.04400844,0.004197106,0.00267916],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1087002,0.0003024977,0.886196,0.0002845924,0.0002986596,0.002805775,0.0001290563,0.001264803,0.00001840451],"genre_scores_gemma":[0.9905864,0.000059513,0.005147289,0.00003749241,0.00001828279,0.004088304,0.000003810082,0.00004208816,0.00001681487],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8818862,"threshold_uncertainty_score":0.9999719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01566747706812895,"score_gpt":0.2727946510647468,"score_spread":0.2571271739966178,"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."}}