{"id":"W2980043203","doi":"10.22215/etd/2016-11443","title":"Multilevel Polar Codes for Grassmannian Signalling","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Grassmannian; Constellation; Coding (social sciences); Algorithm; Polar; Computer science; Partition (number theory); Set (abstract data type); Channel (broadcasting); Constellation diagram; Decoding methods; Theoretical computer science; Polar code; Mathematics; Bit error rate; Telecommunications; Statistics","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":[0.0001968649,0.0003438927,0.0002292049,0.0005436396,0.0003751885,0.0009536208,0.0002745225,0.000463299,0.006971681],"category_scores_gemma":[0.0008234124,0.0001346743,0.0002176494,0.0006232032,0.0006902608,0.0006525923,0.0006946089,0.001259018,0.001826857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005750514,"about_ca_system_score_gemma":0.0004503414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003562353,"about_ca_topic_score_gemma":0.000568054,"domain_scores_codex":[0.9997981,0.00004210388,0.000007552619,0.00002699716,0.00009652622,0.00002871224],"domain_scores_gemma":[0.9998054,0.00007315414,0.00002045652,0.00003770432,0.00004862361,0.00001463944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000359891,0.00001010714,0.00006405993,0.00007324014,0.000004429199,0.0000513614,0.00009920158,0.005740839,0.008793376,0.9343735,0.003257066,0.04749687],"study_design_scores_gemma":[0.00002363519,0.0000487046,0.0002086728,0.0001129976,0.000008712996,0.000239053,0.00004839997,0.04491354,0.01070701,0.8928621,0.05079938,0.00002779758],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05086914,0.008926513,0.7695628,0.002128262,0.0006759559,0.0001121031,0.0005284697,0.0003928748,0.1668039],"genre_scores_gemma":[0.661254,0.01332457,0.2603726,0.0009015588,0.0008673776,0.0002620705,0.0007091056,0.0002081719,0.06210054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006971681,"threshold_uncertainty_score":0.02332264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02548858723365353,"score_gpt":0.3084197226063094,"score_spread":0.2829311353726559,"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."}}