{"id":"W1537569686","doi":"10.1109/pacrim.2005.1517260","title":"Selection in multimode coding for multiple constraints","year":2005,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Multi-mode optical fiber; Coding (social sciences); Computer science; Decoding methods; Encoding (memory); Selection (genetic algorithm); Channel code; Constraint (computer-aided design); Cascade; Algorithm; Theoretical computer science; Artificial intelligence; Mathematics; Telecommunications; Engineering; Optical fiber","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":[0.0004108164,0.0003976581,0.000264777,0.0004144447,0.0003961815,0.000334082,0.0004737933,0.0003927658,0.001348514],"category_scores_gemma":[0.001743276,0.0001656479,0.0002534522,0.0006313749,0.0003974631,0.000842048,0.0004714828,0.0004679914,0.0002631242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000374627,"about_ca_system_score_gemma":0.0003842785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001012997,"about_ca_topic_score_gemma":0.001710436,"domain_scores_codex":[0.9995357,0.0001451633,0.00001819397,0.00006908028,0.000184928,0.0000469195],"domain_scores_gemma":[0.9990448,0.0005770246,0.00008453074,0.0001010778,0.0001595644,0.00003286924],"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":[0.0004902387,0.0001293499,0.00200716,0.0002063434,0.00006697945,0.0007888425,0.0005036464,0.3024285,0.2583564,0.1260322,0.002783099,0.3062072],"study_design_scores_gemma":[0.00003113554,0.000181481,0.0004150285,0.00001763149,0.00001413727,0.0003077657,0.0000315395,0.9242435,0.05046209,0.02051043,0.003755971,0.00002939698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07336372,0.0002803547,0.9211544,0.0001410119,0.00003738897,0.00005770536,0.00004027796,0.000178848,0.004746275],"genre_scores_gemma":[0.6511325,0.0002787136,0.3450779,0.0001344622,0.00004707478,0.0001622587,0.00009053123,0.00005501905,0.003021471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001348514,"threshold_uncertainty_score":0.004511237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02314587946194829,"score_gpt":0.2741299716712371,"score_spread":0.2509840922092889,"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."}}