{"id":"W2001167858","doi":"10.1117/12.468022","title":"<title>Cross-switching in asymmetrical coding for stereoscopic video</title>","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Computer vision; Stereoscopy; Artificial intelligence; Image quality; Coding (social sciences); Multiview Video Coding; Video processing; Video tracking; Image (mathematics); Mathematics","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":[0.0002622058,0.0001865932,0.0001466255,0.000261339,0.0001416443,0.0003528471,0.000385412,0.0002437859,0.01223666],"category_scores_gemma":[0.0006157917,0.00006799396,0.000108796,0.0002408123,0.0002815722,0.0004818577,0.0002252142,0.0003147577,0.002211506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002773067,"about_ca_system_score_gemma":0.0001088151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005308866,"about_ca_topic_score_gemma":0.0009430912,"domain_scores_codex":[0.9998788,0.00002107926,0.00001004289,0.00001775292,0.00006019763,0.0000121418],"domain_scores_gemma":[0.999712,0.00008086723,0.00003351082,0.00006033207,0.00008505533,0.00002815359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007487353,0.0001361259,0.0008923043,0.0003139495,0.00001047391,0.0003920861,0.00009913131,0.001796573,0.6193856,0.02000111,0.01031994,0.345904],"study_design_scores_gemma":[0.0001189728,0.001329753,0.008680448,0.00008905267,0.00005059929,0.001997209,0.00006091809,0.08032015,0.8089324,0.00730915,0.09105051,0.00006078816],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4008936,0.003664397,0.459792,0.001016197,0.002029797,0.0005120998,0.0005810023,0.00372624,0.1277848],"genre_scores_gemma":[0.8371687,0.001272121,0.1265632,0.0003458889,0.0001996989,0.00009460135,0.0005324755,0.0004502271,0.03337301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01223666,"threshold_uncertainty_score":0.0409357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02393242706185511,"score_gpt":0.2749803725378647,"score_spread":0.2510479454760096,"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."}}