{"id":"W2184323220","doi":"10.1109/mnet.2015.7340426","title":"CHetNet: crowdsourcing to heterogeneous cellular networks","year":2015,"lang":"en","type":"article","venue":"IEEE Network","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Heterogeneous network; Crowdsourcing; Computer science; Software deployment; Cellular network; Wireless network; Computer network; Distributed computing; Wireless; Data science; Telecommunications; World Wide Web; Software 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008042293,0.0003511673,0.0004111836,0.0000740821,0.0002290186,0.00032969,0.001249092,0.0002164022,0.0000119127],"category_scores_gemma":[0.000006667029,0.0003463837,0.0001385101,0.0008158586,0.00004998003,0.0002213907,0.0003424416,0.0003347901,0.0003200319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009034071,"about_ca_system_score_gemma":0.0001239626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000273703,"about_ca_topic_score_gemma":0.00001381891,"domain_scores_codex":[0.9970386,0.0001380785,0.0004749513,0.0007305158,0.0005006603,0.001117164],"domain_scores_gemma":[0.9974504,0.0001228507,0.0001399086,0.001105046,0.0001512895,0.001030545],"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":[0.00003734754,0.00003584335,0.0003116926,0.000003383967,0.00003070719,0.0003126998,0.000294047,0.7839384,0.00002038146,0.0004301897,0.1913122,0.02327309],"study_design_scores_gemma":[0.0004026214,0.0001945674,0.00002702486,0.00005683802,0.00001749107,0.00009183507,0.000009644734,0.9145815,0.0001222769,0.002461093,0.08149789,0.0005372074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006076555,0.001130169,0.9726063,0.0003533486,0.0114248,0.0002931307,0.000001084287,0.000440405,0.007674186],"genre_scores_gemma":[0.9683846,0.00001630936,0.01631492,0.003646653,0.01060739,0.00003118296,0.000006926327,0.00004888184,0.0009431925],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.962308,"threshold_uncertainty_score":0.9998988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03322103378672488,"score_gpt":0.2385324216004297,"score_spread":0.2053113878137048,"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."}}