{"id":"W2905156765","doi":"10.1109/tmbmc.2018.2885288","title":"Communication System Design and Analysis for Asynchronous Molecular Timing Channels","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Molecular Biological and Multi-Scale Communications","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Science Foundation","keywords":"Asynchronous communication; Modulation (music); Additive white Gaussian noise; Noise (video); Computer science; Binary number; Algorithm; Gaussian noise; Channel (broadcasting); Bit error rate; Communications system; Signal-to-noise ratio (imaging); Topology (electrical circuits); Electronic engineering; Mathematics; Telecommunications; Physics; Acoustics; Artificial intelligence; Engineering","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":[0.0008572205,0.0007183073,0.000619624,0.0005944975,0.0005990358,0.001155292,0.0006820945,0.0008124649,0.003132382],"category_scores_gemma":[0.002472657,0.0003412604,0.0004702368,0.0005931655,0.0007564119,0.001020399,0.0006591502,0.0007908805,0.0005829086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001661731,"about_ca_system_score_gemma":0.0009695289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00222435,"about_ca_topic_score_gemma":0.001657738,"domain_scores_codex":[0.9991959,0.0002113817,0.00002330135,0.0001136031,0.0003508904,0.0001048723],"domain_scores_gemma":[0.9987532,0.0005940825,0.0002038731,0.00006808644,0.0003444681,0.00003636718],"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":[0.00007708668,0.00004444355,0.0006362236,0.0001466909,0.00003257985,0.0001557939,0.0001544632,0.8234685,0.01928856,0.1353326,0.001490801,0.01917214],"study_design_scores_gemma":[0.000003928932,0.00002089367,0.00007549925,0.000004742153,0.000007021278,0.00003168808,0.00001048947,0.9939516,0.0008380166,0.004429095,0.0006205628,0.000006412548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02660218,0.0006621588,0.9622235,0.0002652445,0.00004753449,0.00007335514,0.00005670522,0.00009475412,0.0099745],"genre_scores_gemma":[0.9135405,0.001309945,0.07658076,0.0001431802,0.0001222151,0.0002482306,0.00009434871,0.00007606878,0.007884765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003132382,"threshold_uncertainty_score":0.01205677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06665745886897742,"score_gpt":0.2864848280001804,"score_spread":0.219827369131203,"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."}}