{"id":"W4225656449","doi":"10.1109/access.2022.3160187","title":"The Tensor Multi-Linear Channel and Its Shannon Capacity","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tensor (intrinsic definition); Notation; Channel (broadcasting); Precoding; Domain (mathematical analysis); Computer science; Mathematics; Theoretical computer science; Topology (electrical circuits); Pure mathematics; MIMO; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001266159,0.0009175871,0.0007107828,0.001046415,0.0007257665,0.002134388,0.0008706109,0.001206286,0.004222984],"category_scores_gemma":[0.00470437,0.0003351411,0.0005618561,0.001249692,0.002907689,0.002993163,0.001434506,0.001517923,0.001007593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373093,"about_ca_system_score_gemma":0.001223082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002213942,"about_ca_topic_score_gemma":0.001389597,"domain_scores_codex":[0.9985274,0.0005090884,0.00006398636,0.0002077766,0.000471386,0.0002203318],"domain_scores_gemma":[0.9966661,0.001948971,0.0003564511,0.000390079,0.0005257851,0.000112635],"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":[0.0000715454,0.00002221613,0.0003374208,0.0001684864,0.00002679245,0.0001537652,0.0001447907,0.1133409,0.003438817,0.8608012,0.003680893,0.01781323],"study_design_scores_gemma":[0.00001404916,0.0000593015,0.0004194035,0.0000766024,0.00002204212,0.0002204133,0.00007961615,0.4961073,0.00261339,0.4912718,0.00904717,0.00006885494],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02741551,0.002241899,0.9283839,0.001621507,0.0001997462,0.00005764202,0.0008082204,0.000281403,0.0389902],"genre_scores_gemma":[0.8598114,0.004329507,0.1211098,0.0008374231,0.0006241262,0.0003914402,0.0005350997,0.0002044773,0.01215672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004222984,"threshold_uncertainty_score":0.01412731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1867681659927632,"score_gpt":0.3862752086236235,"score_spread":0.1995070426308603,"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."}}