{"id":"W2890051339","doi":"10.1049/iet-wss.2017.0095","title":"Performance of rank metric codes for interference constrained wireless sensor networks","year":2018,"lang":"en","type":"article","venue":"IET Wireless Sensor Systems","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Wireless sensor network; Wireless; Coding (social sciences); Computer network; Linear network coding; Additive white Gaussian noise; Wireless network; Channel (broadcasting); Telecommunications; Network packet; Mathematics","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":[0.002760954,0.0009206846,0.0007096062,0.0008530875,0.0005530163,0.001027358,0.0006839164,0.00076454,0.001239981],"category_scores_gemma":[0.01353377,0.0002698761,0.0002351847,0.000911524,0.001232933,0.001166519,0.001456929,0.000617937,0.0002319289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001506405,"about_ca_system_score_gemma":0.002144085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003476063,"about_ca_topic_score_gemma":0.002297771,"domain_scores_codex":[0.9979929,0.000869661,0.00007199207,0.0001480586,0.0006519924,0.0002654723],"domain_scores_gemma":[0.9885548,0.007995221,0.0008955815,0.0007164754,0.001575767,0.0002620427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005973765,0.0000555722,0.001315818,0.0001626163,0.00004372069,0.0001007599,0.0001544816,0.9192442,0.007477753,0.03611947,0.0013596,0.03336862],"study_design_scores_gemma":[0.00001079401,0.0001317639,0.0002227138,0.00001054625,0.000005491389,0.00004380195,0.00002970891,0.9895788,0.001829549,0.007902207,0.0002202945,0.000014313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4128332,0.002825354,0.5663234,0.0009824381,0.0001391352,0.0001540008,0.0005456309,0.0008457782,0.01535102],"genre_scores_gemma":[0.979185,0.0004335055,0.0189633,0.00006908611,0.00002473563,0.00007543181,0.0001795748,0.00003017314,0.001039283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003476063,"threshold_uncertainty_score":0.01460147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03693292611355117,"score_gpt":0.2747115808643892,"score_spread":0.2377786547508381,"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."}}