{"id":"W4417469983","doi":"10.1109/jiot.2025.3645830","title":"Signal Recovery and Multisource Localization in Turbulent Molecular Communication With Obstacle Based on the Internet of Nano Things","year":2025,"lang":"","type":"article","venue":"IEEE Internet of Things Journal","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of British Columbia","funders":"China University of Mining and Technology; National Natural Science Foundation of China","keywords":"Obstacle; Compressed sensing; Inverse problem; Nanosensor; Regularization (linguistics); SIGNAL (programming language); Grid; Iterative method; Wireless","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004319723,0.0003842269,0.0004147587,0.000257762,0.0003062326,0.0003999652,0.0005259925,0.0005594163,0.0004680563],"category_scores_gemma":[0.001160916,0.0001907975,0.0004897985,0.0004087552,0.0006072083,0.000864287,0.0008340267,0.0006586977,0.0001126968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002901794,"about_ca_system_score_gemma":0.0004963318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002186364,"about_ca_topic_score_gemma":0.001510124,"domain_scores_codex":[0.9997843,0.00006239139,0.000008959507,0.00004148504,0.00008043096,0.00002240064],"domain_scores_gemma":[0.9996575,0.0001801067,0.00005772114,0.00002561846,0.00006138059,0.00001775655],"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":[0.0001550111,0.00003967533,0.00117266,0.0001440964,0.00004437417,0.000334075,0.0001755602,0.9052868,0.01556904,0.03090728,0.001332624,0.04483873],"study_design_scores_gemma":[0.00000419611,0.00001577575,0.00007455623,0.000002393271,0.000002631434,0.0000294772,0.00001127781,0.9963474,0.0009108371,0.00236094,0.0002352862,0.000005254486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0241849,0.0001793428,0.9740458,0.0001934359,0.00003314428,0.00001487657,0.00002043093,0.00008902937,0.001239094],"genre_scores_gemma":[0.7782826,0.0005401403,0.2180132,0.0001822973,0.00004206699,0.00009114511,0.0001311475,0.00004023354,0.002677246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002186364,"threshold_uncertainty_score":0.004347265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007381408185338932,"score_gpt":0.2119088954244014,"score_spread":0.2045274872390624,"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."}}