{"id":"W2031515082","doi":"10.1109/tnb.2014.2337239","title":"Optimal Receiver Design for Diffusive Molecular Communication With Flow and Additive Noise","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on NanoBioscience","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":206,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Molecular communication; Detector; Noise (video); Upper and lower bounds; Sequence (biology); Detection theory; Flow (mathematics); Bit error rate; Signal-to-noise ratio (imaging)","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":[0.0001742505,0.000135403,0.0001151011,0.00008701203,0.0002523938,0.00004494877,0.000249948,0.00006446282,0.0000130064],"category_scores_gemma":[0.000006415919,0.0001251968,0.00003603768,0.0002490822,0.0001693126,0.0001300244,0.000001829,0.000136772,0.000005941423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003572709,"about_ca_system_score_gemma":0.00001573582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004237208,"about_ca_topic_score_gemma":0.00001083831,"domain_scores_codex":[0.9993127,0.00008374229,0.0001181288,0.0001882495,0.0001165794,0.0001806284],"domain_scores_gemma":[0.9991742,0.0002099788,0.00002873639,0.0004367958,0.00006956515,0.00008073418],"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":[0.00009306773,0.00008366191,9.760673e-7,0.00001628516,0.00003303389,8.178996e-7,0.0004311207,0.8558484,0.06655678,0.0002384881,0.0002073565,0.07649007],"study_design_scores_gemma":[0.0005944808,0.0002085289,0.00003147901,0.00005664069,0.00002836317,0.000008176885,0.00003432246,0.8127562,0.1842684,0.00003975855,0.001761508,0.0002120885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00997194,0.0001160234,0.9889163,0.0001018543,0.00007571917,0.0003891181,0.00001949244,0.0001152749,0.0002942558],"genre_scores_gemma":[0.8663231,0.0003572152,0.1329597,0.0001266176,0.000004458131,0.0001489755,0.00000432786,0.00002055157,0.00005515658],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8563511,"threshold_uncertainty_score":0.510538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008757491118804157,"score_gpt":0.202282830559547,"score_spread":0.1935253394407428,"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."}}