{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003066211,0.0005859285,0.0005793474,0.0003528782,0.0003649573,0.0004920984,0.0008484862,0.0009305124,0.002564209],"category_scores_gemma":[0.0005844365,0.0003551236,0.0005247581,0.000297165,0.0003027268,0.0006771917,0.0006452344,0.0009321403,0.00137269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004133419,"about_ca_system_score_gemma":0.001074438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004729669,"about_ca_topic_score_gemma":0.008351919,"domain_scores_codex":[0.9998405,0.00001644469,0.00000952968,0.00004235335,0.00005771806,0.00003340675],"domain_scores_gemma":[0.9997855,0.00003563246,0.00001453533,0.00001830368,0.0001264884,0.00001957144],"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":[0.0002759836,0.0002063417,0.001507774,0.0001835572,0.0001294133,0.0002228328,0.00007514567,0.33288,0.08312106,0.01036175,0.006979123,0.564057],"study_design_scores_gemma":[0.000005463078,0.00004438925,0.0002056577,0.00000667764,0.00001390798,0.00004304275,0.000007633082,0.9905694,0.00699248,0.0009584829,0.001144229,0.000008625766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01096002,0.0002140599,0.9857171,0.0001334858,0.0001095458,0.00004243544,0.00008523414,0.0007962738,0.001941993],"genre_scores_gemma":[0.5631942,0.0004548052,0.4260935,0.000372311,0.0001037328,0.0002113423,0.00064042,0.00013779,0.008791922],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004729669,"threshold_uncertainty_score":0.009404242,"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."}}