{"id":"W7001886350","doi":"","title":"A MARL Approach for Finding Optimal Positions for VANET Aerial Base-stations on a Sparse Highway","year":2021,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"St. Francis Xavier University","keywords":"Vehicular ad hoc network; Reinforcement learning; Function (biology); Component (thermodynamics); Work (physics); Mobile device; Training (meteorology); Mobile radio; Mobile telephony; Block (permutation group theory)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007217808,0.00124879,0.001366422,0.0006807019,0.0005914354,0.0009700499,0.001982897,0.001859905,0.005223539],"category_scores_gemma":[0.00318912,0.0008669498,0.0008174015,0.0005632038,0.000794713,0.0009417277,0.001676274,0.001831583,0.0008959993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001213873,"about_ca_system_score_gemma":0.001351662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01301471,"about_ca_topic_score_gemma":0.01036027,"domain_scores_codex":[0.999575,0.0001165716,0.00002118805,0.0001195155,0.00007828756,0.00008949661],"domain_scores_gemma":[0.9987005,0.0007907429,0.0001337236,0.00006460195,0.0002060256,0.0001043273],"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":[0.00003341127,0.00002523675,0.0003259402,0.00002897574,0.00001185298,0.00004097544,0.00002367177,0.9813249,0.0003025882,0.002209323,0.0007103176,0.01496292],"study_design_scores_gemma":[0.000006689235,0.000012501,0.00001922342,0.00000250931,0.000001569491,0.000003827146,0.000004738061,0.9990063,0.00005762449,0.0007487075,0.0001349046,0.000001371687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03187028,0.0004413743,0.9605671,0.0004986105,0.00008821199,0.0001183575,0.0001718386,0.00103473,0.005209519],"genre_scores_gemma":[0.7410886,0.0002264752,0.249557,0.0004123879,0.00009847119,0.0003606545,0.0004735167,0.0001899342,0.007592994],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01301471,"threshold_uncertainty_score":0.02587789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009830932664700674,"score_gpt":0.1926232952556091,"score_spread":0.1827923625909084,"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."}}