{"id":"W4403579261","doi":"10.1016/j.iot.2024.101393","title":"Design of a turbo-based deep semantic autoencoder for marine Internet of Things","year":2024,"lang":"en","type":"article","venue":"Internet of Things","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Autoencoder; Computer science; Turbo; Artificial intelligence; Natural language processing; Deep learning; Engineering; Automotive engineering","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":[0.0006677548,0.0001723262,0.0003312903,0.0003251176,0.00001072676,0.00007876572,0.001049976,0.0001002961,0.00005257113],"category_scores_gemma":[0.0001539686,0.0001572228,0.000163951,0.0003061923,0.00009895362,0.0007026633,0.0002358196,0.0001377172,0.00000968757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006713635,"about_ca_system_score_gemma":0.00009970036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004200745,"about_ca_topic_score_gemma":0.000003502362,"domain_scores_codex":[0.9982573,0.0000751535,0.0006976922,0.0004056781,0.0003830996,0.0001810878],"domain_scores_gemma":[0.9982409,0.0005946146,0.0003951121,0.000427369,0.0002955333,0.00004648806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000715245,0.001053935,0.002917103,0.007549847,0.0009379699,0.0000342979,0.06550353,0.04400201,0.3564428,0.3105905,0.007115386,0.2031374],"study_design_scores_gemma":[0.000228603,0.0002264026,0.0002303061,0.0004563968,0.00002461414,0.000004226072,0.00001194535,0.8326828,0.1579743,0.007923738,0.0001235169,0.0001131007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0154194,0.00009035923,0.9825968,0.0007310786,0.0004076893,0.0003680577,0.000001626942,0.0001389912,0.0002460421],"genre_scores_gemma":[0.7974649,0.000001388786,0.2018349,0.0001366234,0.00001592443,0.00002360457,0.000007279225,0.00001875708,0.0004965888],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7886809,"threshold_uncertainty_score":0.6411359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.028628522595442,"score_gpt":0.2613450589693783,"score_spread":0.2327165363739363,"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."}}