{"id":"W4256281211","doi":"10.32920/ryerson.14652186.v1","title":"Designing and evaluating a system for the effective analysis of sign language video content for the improvement of video quality","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hearing Impairment and Communication","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Videotelephony; Multimedia; CLARITY; Quality (philosophy); Sign language; Set (abstract data type); Video quality; Channel (broadcasting); Subjective video quality; Online video; Human–computer interaction; Visual communication; Artificial intelligence; Telecommunications; Image quality","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008585238,0.0009091777,0.0007922042,0.001313382,0.000654588,0.001762469,0.001835705,0.001445145,0.003122418],"category_scores_gemma":[0.01989582,0.0005208061,0.0005126509,0.0005724852,0.0008526256,0.001796699,0.001133351,0.0005359051,0.001093164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012795,"about_ca_system_score_gemma":0.00158575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0024624,"about_ca_topic_score_gemma":0.002310544,"domain_scores_codex":[0.9941765,0.002560786,0.0005307674,0.0008840832,0.001489384,0.0003585751],"domain_scores_gemma":[0.9868596,0.007405789,0.0004480536,0.0008160317,0.003787208,0.0006832915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004970564,0.004811658,0.02577183,0.002213409,0.0002104606,0.001057424,0.00541942,0.009215244,0.4097082,0.001726281,0.00470318,0.5301923],"study_design_scores_gemma":[0.002666336,0.02635408,0.1136813,0.0005567887,0.0009587408,0.002409495,0.004223109,0.26726,0.5552518,0.001783885,0.02433467,0.0005199947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6670521,0.0002360449,0.3112567,0.000233184,0.0001061155,0.008697841,0.0004455134,0.009052898,0.00291962],"genre_scores_gemma":[0.4677405,0.0001246729,0.5258172,0.0001263668,0.00002721057,0.002787376,0.0004493145,0.0002492244,0.002678085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008585238,"threshold_uncertainty_score":0.04540366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1632799305296158,"score_gpt":0.4457821817920402,"score_spread":0.2825022512624243,"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."}}