{"id":"W2982094032","doi":"10.1109/tnb.2019.2949261","title":"Performance Enhancement of Diffusion-Based Molecular Communication","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on NanoBioscience","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Molecular communication; Degradation (telecommunications); Interference (communication); Computer science; Metric (unit); Molecular biophysics; Molecule; SIGNAL (programming language); Biological system; Probability of error; Signal-to-noise ratio (imaging); Transmission (telecommunications); Diffusion; Algorithm; Chemistry; Physics; Telecommunications; Transmitter; Engineering; Nuclear magnetic resonance","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":[0.0001355347,0.0001031961,0.000111649,0.000116802,0.00008065654,0.00001136962,0.0004200462,0.00005263256,0.0001674169],"category_scores_gemma":[0.000001141417,0.0001041742,0.00005975089,0.0004236817,0.00007554847,0.00008176462,0.000001926301,0.000134252,0.00007359827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004623144,"about_ca_system_score_gemma":0.00002462471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006688529,"about_ca_topic_score_gemma":0.000002701933,"domain_scores_codex":[0.9992739,0.00003676056,0.0002016421,0.0001280865,0.000211818,0.0001477945],"domain_scores_gemma":[0.999049,0.00004657348,0.00004032227,0.0007769188,0.0000439096,0.00004328158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009922709,0.0001021329,0.0000549486,0.00003704827,0.000006409694,1.367602e-7,0.00005145142,0.1944258,0.7938691,0.00004210962,0.00001465429,0.01138629],"study_design_scores_gemma":[0.0001937163,0.00005780104,0.0001110852,0.00006758802,0.000004672866,4.433104e-7,0.000006109867,0.3494435,0.6496829,0.000002641194,0.0003403944,0.000089155],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5669901,0.00007982196,0.4303156,0.00003528895,0.0001839298,0.0001678172,0.000002093668,0.00006281171,0.002162572],"genre_scores_gemma":[0.9966131,0.0002899971,0.002826957,0.00009717816,0.000001122481,0.00002707903,0.000002174868,0.00001243577,0.0001298866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4296231,"threshold_uncertainty_score":0.4248101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006055663790579291,"score_gpt":0.1986850221193339,"score_spread":0.1926293583287546,"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."}}