{"id":"W2079057834","doi":"10.1007/s11036-011-0346-y","title":"Advances in Green Mobile Networks","year":2011,"lang":"en","type":"article","venue":"Mobile Networks and Applications","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Enabling; Computer science; Macrocell; Backhaul (telecommunications); Telecommunications; Femtocell; Greenhouse gas; Efficient energy use; Energy consumption; Environmental economics; Cellular network; Wireless; Base station; Electrical engineering; 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":[0.0002313582,0.0002059636,0.0002498775,0.00007297989,0.0002046828,0.00007391324,0.0005796009,0.0001488254,0.00004247448],"category_scores_gemma":[4.349065e-7,0.0001931155,0.00005446126,0.0006029995,0.0001231319,0.0004033255,0.0002341238,0.0002722245,0.00001374112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001889889,"about_ca_system_score_gemma":0.00002592731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005636416,"about_ca_topic_score_gemma":0.00006272498,"domain_scores_codex":[0.9984581,0.00004164079,0.0003704736,0.000554833,0.0001228182,0.0004521553],"domain_scores_gemma":[0.9988979,0.0001085976,0.0001206403,0.0006275207,0.00004868901,0.0001966141],"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":[0.000006899538,0.0001415875,0.001977565,0.000007781478,0.000005693439,0.000008656925,0.00008231695,0.05953359,5.580159e-7,0.02617962,0.0003872678,0.9116685],"study_design_scores_gemma":[0.0002450595,0.00009148337,0.0007284615,0.00002284265,0.000008680087,0.00001588949,0.00005587334,0.9006452,0.000001291575,0.004013516,0.09390564,0.0002660418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004776454,0.0087712,0.9755253,0.0000210673,0.0001383549,0.0008427124,0.000002487921,0.0001520452,0.01406924],"genre_scores_gemma":[0.9882084,0.006269414,0.00186517,0.0002985978,0.0003608755,0.00272018,0.00001769345,0.00001810412,0.0002416293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9877307,"threshold_uncertainty_score":0.7875022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302731511527714,"score_gpt":0.2326066392989615,"score_spread":0.2195793241836844,"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."}}