{"id":"W4290996275","doi":"10.1109/icc45855.2022.9839216","title":"Detection Interval Optimization for Diffusion-based Molecular Communication","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Molecular communication; Interference (communication); Computer science; Bit error rate; Interval (graph theory); Detection theory; Signal-to-noise ratio (imaging); SIGNAL (programming language); Intersymbol interference; Key (lock); Noise (video); Electronic engineering; Communications system; Algorithm; Telecommunications; Mathematics; Artificial intelligence; Engineering; Transmitter; Detector; Channel (broadcasting); Decoding methods","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000431147,0.000219871,0.0001859434,0.0003440576,0.0007669244,0.0001232518,0.002599489,0.00008321054,0.001027452],"category_scores_gemma":[0.00006368611,0.0002789837,0.0001716267,0.0003740254,0.00009959592,0.0001373279,0.0003992213,0.0006484074,0.00001961257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004009966,"about_ca_system_score_gemma":0.00006716825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002529038,"about_ca_topic_score_gemma":0.00007852759,"domain_scores_codex":[0.998276,0.0003680287,0.0004792042,0.0002444475,0.0004209321,0.0002113667],"domain_scores_gemma":[0.9970363,0.0003048097,0.0001711735,0.002125298,0.0002837071,0.00007868418],"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":[0.0001012991,0.0003656222,0.00002596794,0.0000145523,0.0001251114,9.360959e-7,0.0002355728,0.9252906,0.02157182,0.0318417,0.001858202,0.0185686],"study_design_scores_gemma":[0.0006843279,0.00009265635,0.00002825174,0.00003233763,0.00002383721,0.000004337132,0.0002066888,0.9663737,0.002278143,0.001069341,0.02893785,0.0002685064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008408044,0.000662509,0.949496,0.006831257,0.001393172,0.001222306,0.000321904,0.0007095892,0.03095517],"genre_scores_gemma":[0.9814125,0.0005851759,0.01362599,0.0006654897,0.00003021971,0.00191296,0.001540573,0.00006375935,0.000163314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9730045,"threshold_uncertainty_score":0.9999662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04596066883208022,"score_gpt":0.2950425996299517,"score_spread":0.2490819307978714,"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."}}