{"id":"W4402474985","doi":"10.1109/ccece59415.2024.10667089","title":"Boosting Edge-to-Cloud Data Transmission Efficiency with Semantic Transcoding","year":2024,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Transcoding; Boosting (machine learning); Computer science; Cloud computing; Artificial intelligence; Computer network","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":[0.0004747918,0.0006344607,0.0004342005,0.0005914332,0.0004425242,0.001044097,0.001016004,0.0004008961,0.001263259],"category_scores_gemma":[0.002039585,0.0001358062,0.0002798172,0.0007795967,0.0004936269,0.002328197,0.0009885973,0.0007927695,0.0003942669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005300377,"about_ca_system_score_gemma":0.0006449128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001769656,"about_ca_topic_score_gemma":0.002919902,"domain_scores_codex":[0.9996903,0.00004887896,0.00001926217,0.00005490894,0.0001354334,0.00005126806],"domain_scores_gemma":[0.9992228,0.0002246935,0.00004846286,0.0001804747,0.0002910743,0.00003251124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008840028,0.0004458572,0.002320076,0.0002852943,0.0001100678,0.0005621398,0.0004636077,0.1363431,0.2898321,0.04329046,0.009543351,0.5159199],"study_design_scores_gemma":[0.00003187763,0.000101415,0.0004552869,0.00001498947,0.00004721995,0.000327001,0.0001562186,0.8661274,0.1064582,0.0208872,0.005361138,0.0000319944],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07413752,0.0005364732,0.9175411,0.0002602377,0.0001176451,0.00005404542,0.00009245416,0.001298036,0.005962507],"genre_scores_gemma":[0.827967,0.0004507714,0.1690148,0.0001988792,0.00006118613,0.00003221603,0.0001961365,0.0001951724,0.001883781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001769656,"threshold_uncertainty_score":0.004226029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03574096676471816,"score_gpt":0.2740067997856927,"score_spread":0.2382658330209745,"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."}}