{"id":"W3016369572","doi":"10.1109/ojcoms.2020.2987543","title":"A Tensor Based Framework for Multi-Domain Communication Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Tensor (intrinsic definition); Theoretical computer science; Distributed computing; Topology (electrical circuits); Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010632,0.0001360468,0.00032945,0.0000181837,0.0009324605,0.0002455506,0.005173392,0.0001098556,0.00001596687],"category_scores_gemma":[0.0004606192,0.0001011005,0.0004886836,0.0003603372,0.0002009601,0.0001949124,0.0004498437,0.0005825247,0.000008402845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001015953,"about_ca_system_score_gemma":0.0001563871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007663195,"about_ca_topic_score_gemma":0.000004452226,"domain_scores_codex":[0.9983277,0.0004731407,0.0007331064,0.0001140417,0.0002077268,0.0001443035],"domain_scores_gemma":[0.9946728,0.001635878,0.001066336,0.001890228,0.0006095628,0.0001251465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001145288,0.001632658,0.0005100283,0.0001744961,0.0005911462,2.706229e-7,0.009868382,0.00108106,0.004196978,0.7345741,0.2466883,0.0005680766],"study_design_scores_gemma":[0.008307254,0.0003030807,0.0008122381,0.001684055,0.0008646594,0.00009111923,0.01843335,0.3490686,0.001736417,0.2930336,0.3247045,0.0009610929],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005708036,0.000612488,0.8753108,0.1156853,0.0001579976,0.001964998,0.0001177811,0.00004583756,0.0003967773],"genre_scores_gemma":[0.2900065,0.0000993397,0.707381,0.002193152,0.00005721884,0.0001421677,0.000005955317,0.00002780295,0.00008684739],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4415404,"threshold_uncertainty_score":0.9613535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3011576138005281,"score_gpt":0.4345508135315274,"score_spread":0.1333931997309993,"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."}}