{"id":"W3131063976","doi":"10.1109/lcomm.2021.3058731","title":"Data-Oriented View for Convolutional Coding With Adaptive Irregular Constellations","year":2021,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Carleton University","funders":"Academy of Finland","keywords":"Convolutional code; Computer science; Algorithm; Decoding methods; Fading; Encoder; Network packet; Coding (social sciences); Forward error correction; Performance improvement; Constellation; Viterbi algorithm; Viterbi decoder; Coding gain; Theoretical computer science; Computer network; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001597221,0.000170245,0.0002084111,0.00009180631,0.0003802849,0.00003618431,0.001264627,0.00005528589,0.00001749515],"category_scores_gemma":[0.00004458135,0.0001920297,0.00004587104,0.0004023292,0.0003350346,0.0003701877,0.0002638666,0.0002625958,0.00001150106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001382746,"about_ca_system_score_gemma":0.00006973308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003896552,"about_ca_topic_score_gemma":0.00007882198,"domain_scores_codex":[0.9989842,0.0001073328,0.0003262502,0.0002305102,0.0001409066,0.0002107539],"domain_scores_gemma":[0.9948203,0.0006350482,0.00008366016,0.004127493,0.0002732402,0.00006023761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008613974,0.0005963783,0.0008066418,0.0003825079,0.001679662,0.00001412657,0.001240486,0.1387527,0.2783013,0.4217491,0.1189768,0.0374143],"study_design_scores_gemma":[0.001126525,0.00003379544,0.0002900851,0.0003921361,0.0001567144,0.0000715055,0.0005613342,0.4270828,0.0216598,0.0007392977,0.5470666,0.0008194771],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00125002,0.002300605,0.9891721,0.004089287,0.00009741566,0.0004792619,0.0005644232,0.0006930843,0.001353797],"genre_scores_gemma":[0.5303679,0.00119496,0.4655201,0.0005981247,0.00003179154,0.0003000966,0.001894753,0.00005437939,0.0000378289],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5291179,"threshold_uncertainty_score":0.7830743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07129909907981043,"score_gpt":0.2976232238698447,"score_spread":0.2263241247900343,"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."}}