{"id":"W3034332820","doi":"10.1109/tii.2020.3000502","title":"Multipath Communication With Deep Q-Network for Industry 4.0 Automation and Orchestration","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Orchestration; Multipath TCP; Computer science; Automation; Convergence (economics); Multipath propagation; Computer network; Distributed computing; 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":[0.0002949886,0.000156225,0.0001650129,0.00006926242,0.0004355343,0.0002633168,0.0003067027,0.000311297,0.000001157342],"category_scores_gemma":[0.00001842681,0.0001435558,0.00003779065,0.0004385697,0.00004542359,0.0009908866,0.000005443438,0.0006263911,0.000006938565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004802715,"about_ca_system_score_gemma":0.0001129416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001001859,"about_ca_topic_score_gemma":0.000003507746,"domain_scores_codex":[0.9989601,0.00005230551,0.0004436893,0.000126267,0.00019187,0.0002258],"domain_scores_gemma":[0.9990383,0.0002440259,0.0002200824,0.0002641501,0.0001012454,0.0001321646],"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":[0.0002349185,0.0001188233,0.0001350671,0.0001115012,0.0001165993,9.527321e-7,0.01933632,0.2285054,0.00003540968,0.001189287,0.00551565,0.7447001],"study_design_scores_gemma":[0.001352864,0.0003981073,0.00003889159,0.00007074718,0.0000268051,0.000007277674,0.0002427914,0.9941366,0.001292539,0.0001303352,0.002103018,0.0002000035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02333617,0.000007736095,0.9736713,0.001058592,0.0009771835,0.0005388535,0.00000210708,0.0002380524,0.0001700074],"genre_scores_gemma":[0.7362071,0.0000129736,0.2616281,0.0008472298,0.001170612,0.00007827424,0.00001803884,0.00001786411,0.00001989336],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7656313,"threshold_uncertainty_score":0.5854034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06521271844045394,"score_gpt":0.2568193693334363,"score_spread":0.1916066508929823,"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."}}