{"id":"W3014973596","doi":"10.48550/arxiv.2004.00760","title":"Consistent Multiple Sequence Decoding","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Decoding methods; Computer science; Fusion mechanism; Context (archaeology); Sequential decoding; List decoding; Sequence (biology); Closed captioning; Task (project management); Algorithm; Pattern recognition (psychology); Artificial intelligence; Theoretical computer science; Fusion; Image (mathematics); Concatenated error correction code; Block code","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":[0.001672647,0.001265826,0.001230037,0.0009180309,0.0007668841,0.001918245,0.002883487,0.001797487,0.008007876],"category_scores_gemma":[0.008788085,0.0005209888,0.001130528,0.001205842,0.001584067,0.003793302,0.002665335,0.002190476,0.004450634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005102,"about_ca_system_score_gemma":0.002473739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004076033,"about_ca_topic_score_gemma":0.006195734,"domain_scores_codex":[0.9981942,0.0005017528,0.0001185369,0.0005467821,0.0004993865,0.0001393939],"domain_scores_gemma":[0.9963003,0.001207562,0.0002279069,0.00118543,0.0009616939,0.0001172199],"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":[0.0005315589,0.0001664337,0.001836883,0.0004511295,0.0001547441,0.0005604203,0.0005663344,0.1883994,0.03354048,0.1601062,0.01950554,0.5941809],"study_design_scores_gemma":[0.00002552698,0.0001044989,0.0002216187,0.00003970813,0.00003655859,0.0002868832,0.00007810324,0.8414507,0.0284684,0.120223,0.009023246,0.00004173701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006859002,0.0002427996,0.984993,0.0002872898,0.0001189659,0.00006660207,0.0002803009,0.002970084,0.004181978],"genre_scores_gemma":[0.3665358,0.0004369816,0.6157286,0.0005865743,0.0001809345,0.0002441548,0.001871944,0.001666384,0.01274866],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008007876,"threshold_uncertainty_score":0.02678907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1794902213038457,"score_gpt":0.2272252372732713,"score_spread":0.04773501596942556,"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."}}