{"id":"W2916809502","doi":"10.1109/glocom.2018.8647545","title":"Matching-Game for User-Fog Assignment","year":2018,"lang":"en","type":"article","venue":"","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Matching (statistics); Human–computer interaction; Mathematics","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":[0.00004327658,0.00004515338,0.00004265724,0.00002649295,0.0000248648,0.00001107003,0.00003772596,0.00002315978,0.0004054104],"category_scores_gemma":[0.000001864758,0.00004251011,0.00002049374,0.00006226102,0.00001310382,0.00004934467,0.000001267841,0.00002304152,0.0000755794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001559896,"about_ca_system_score_gemma":0.000005347394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007115453,"about_ca_topic_score_gemma":0.0001150834,"domain_scores_codex":[0.9997039,0.000001107352,0.0001086164,0.0000565659,0.00004375214,0.00008609932],"domain_scores_gemma":[0.9998333,0.00001344536,0.000005650936,0.000088565,0.00003699147,0.00002199468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002039854,0.0001372524,0.001734971,0.0001692806,0.0001643503,7.229253e-7,0.003872847,0.01211247,0.06974963,0.8332287,0.06607577,0.01273359],"study_design_scores_gemma":[0.0009518915,0.0001172393,0.02792116,0.00001647814,0.00002665976,0.000001109299,0.0004536084,0.009123673,0.05618422,0.009846794,0.8949938,0.0003633761],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1866753,0.000003208302,0.8002069,0.0002028849,0.0003008764,0.0001801972,0.00001578824,0.0003704384,0.0120444],"genre_scores_gemma":[0.9894418,0.000001010552,0.008683348,0.0002111712,0.00007045312,0.00004599663,0.00001948449,0.00001041817,0.001516362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.828918,"threshold_uncertainty_score":0.4438959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0133066818637505,"score_gpt":0.2463965768975278,"score_spread":0.2330898950337773,"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."}}