{"id":"W4401880034","doi":"10.1109/compsac61105.2024.00111","title":"Optimizing WDM Network Restoration with Deep Reinforcement Learning and Graph Neural Networks Integration","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Reinforcement learning; Computer science; Wavelength-division multiplexing; Artificial neural network; Graph; Artificial intelligence; Distributed computing; Theoretical computer science","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":[0.0006311466,0.0008574365,0.000665058,0.0004158996,0.0003124004,0.0005850844,0.001011161,0.001064574,0.001242074],"category_scores_gemma":[0.002167078,0.0003632228,0.000390305,0.0003510564,0.0006402975,0.0008287304,0.0008983425,0.001170552,0.0001479262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00127733,"about_ca_system_score_gemma":0.001210862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01027562,"about_ca_topic_score_gemma":0.01238192,"domain_scores_codex":[0.9997672,0.00005928781,0.00001019523,0.00005590238,0.00004506888,0.00006221984],"domain_scores_gemma":[0.9991705,0.0004728663,0.0001144561,0.00005115722,0.0001394729,0.00005152066],"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":[0.00001427978,0.00002469211,0.0002322672,0.00001199617,0.00001036895,0.00001805828,0.000007562371,0.9872674,0.0004254079,0.001177672,0.0002572133,0.010553],"study_design_scores_gemma":[0.000001622388,0.000004719593,0.00001667141,7.42234e-7,0.000001240051,0.000001775101,0.000001188766,0.999213,0.00008563927,0.0006377844,0.00003488282,7.377246e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08176408,0.0004432997,0.9116578,0.000612365,0.00009016674,0.00006168689,0.00005838204,0.0008948814,0.004417396],"genre_scores_gemma":[0.9295198,0.0001214709,0.06785239,0.0001868163,0.00003106231,0.00006701901,0.00007103518,0.00004784118,0.00210252],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01027562,"threshold_uncertainty_score":0.02043158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00687236876128234,"score_gpt":0.2131995469998509,"score_spread":0.2063271782385686,"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."}}