{"id":"W3101875731","doi":"","title":"1Optimal Receiver Design for Diffusive Molecular Communication With Flow and Additive Noise","year":2016,"lang":"en","type":"article","venue":"","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":198,"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; Computer science; Algorithm; Intersymbol interference; Noise (video); Transmitter; Interference (communication); Upper and lower bounds; Detection theory; Filter (signal processing); Bit error rate; Electronic engineering; Mathematics; Telecommunications; Decoding methods; Artificial intelligence","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":[0.00008222571,0.0001014447,0.00009474027,0.0000341857,0.00005560582,0.00001732059,0.0001381053,0.00005416289,0.00006665647],"category_scores_gemma":[0.00001595225,0.00006796401,0.0000223538,0.00005972866,0.00005556276,0.0000810405,0.00003390279,0.00005078531,0.000008628118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000242037,"about_ca_system_score_gemma":0.000008059101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001928599,"about_ca_topic_score_gemma":0.000006468345,"domain_scores_codex":[0.9995748,0.00005190452,0.00009189206,0.00009949857,0.00005673854,0.0001251606],"domain_scores_gemma":[0.9992679,0.0002012094,0.00001986225,0.0003893943,0.00006714755,0.00005453428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006786724,0.0001488037,0.0001203647,0.00006521717,0.0006325719,0.0000118297,0.001154831,0.03091436,0.1458649,0.01891231,0.02963964,0.7718565],"study_design_scores_gemma":[0.005963939,0.0004282999,0.001074529,0.000345331,0.0001309719,0.00002800159,0.0001444219,0.8118194,0.1312061,0.001656206,0.04617821,0.00102454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004031003,0.0007164408,0.9908304,0.0003035816,0.00001238129,0.0003761795,0.00001123928,0.0001253682,0.003593397],"genre_scores_gemma":[0.6994728,0.001310643,0.2985998,0.0001230793,0.000008772212,0.0001594782,0.00002287325,0.00003378922,0.0002687481],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7809051,"threshold_uncertainty_score":0.2771492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008505839802931028,"score_gpt":0.1932316032222736,"score_spread":0.1847257634193425,"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."}}