{"id":"W2341777409","doi":"10.1109/isit.2016.7541454","title":"On the capacity of diffusion-based molecular timing channels","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Channel (broadcasting); Noise (video); Diffusion; Upper and lower bounds; Channel capacity; Molecular communication; Computer science; Statistical physics; Topology (electrical circuits); Algorithm; Physics; Electronic engineering; Telecommunications; Mathematics; Engineering; Mathematical analysis; Combinatorics; Transmitter","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":[0.0001835463,0.0001857624,0.0001911358,0.00007025739,0.00003427783,0.00001687279,0.0005333026,0.0001998257,0.0003306525],"category_scores_gemma":[0.00004384971,0.0001141858,0.0001536962,0.00005785601,0.00004474691,0.000006471497,0.0002101758,0.0003825497,0.00002851589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003480812,"about_ca_system_score_gemma":0.00001809186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001186581,"about_ca_topic_score_gemma":0.000001893645,"domain_scores_codex":[0.9992299,0.0000883047,0.0002165538,0.0001410817,0.0001804462,0.0001437132],"domain_scores_gemma":[0.9984273,0.0002159483,0.00006202974,0.001209087,0.0000438136,0.00004178059],"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":[0.00002322627,0.000136177,0.00002458,0.0004161824,0.0003078272,0.000007448252,0.0002632534,0.7801758,0.1093733,0.09590098,0.00852612,0.004845122],"study_design_scores_gemma":[0.0003146941,0.00002165082,0.00004459385,0.0009101425,0.00003339629,5.72042e-7,0.000007493752,0.759747,0.2247856,0.01180285,0.001876806,0.0004552556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1683564,0.0003687386,0.7708991,0.001205509,0.0005861193,0.0005043686,0.0000180332,0.0002860388,0.05777569],"genre_scores_gemma":[0.9986123,0.00005021227,0.0008277073,0.0003011902,0.00002461123,0.00004828837,0.00001126639,0.00003538066,0.00008901184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.830256,"threshold_uncertainty_score":0.4656361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02691371325963316,"score_gpt":0.2215761684629249,"score_spread":0.1946624552032917,"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."}}