{"id":"W1998914666","doi":"10.1504/ijsnet.2008.019252","title":"Efficient aggregation using first hop selection in WSNs","year":2008,"lang":"en","type":"article","venue":"International Journal of Sensor Networks","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Heuristics; Wireless sensor network; Data aggregator; Overhead (engineering); Flow network; Integer programming; Computer network; Distributed computing; Mathematical optimization; Algorithm; Mathematics","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":[0.001179274,0.0006434381,0.0006972775,0.0005677398,0.0005325195,0.0007501767,0.0006426846,0.0004406065,0.0006059068],"category_scores_gemma":[0.002136707,0.0003195784,0.0003381435,0.0007122749,0.000476866,0.001242345,0.0005740299,0.000395889,0.0001829959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004080229,"about_ca_system_score_gemma":0.0003840101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006512285,"about_ca_topic_score_gemma":0.0006723791,"domain_scores_codex":[0.9995561,0.0002101802,0.00001866723,0.00006872311,0.0000990351,0.00004719974],"domain_scores_gemma":[0.9991261,0.0005354562,0.0001364255,0.00008273642,0.00008159265,0.0000376611],"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":[0.0001666006,0.00009583127,0.001632431,0.0002331494,0.00008114787,0.0002535064,0.0002832225,0.8251072,0.01705465,0.04167451,0.002037049,0.1113807],"study_design_scores_gemma":[0.00002155912,0.0002132947,0.0003512647,0.00001807689,0.00003070282,0.0001336263,0.00005504049,0.9682238,0.005024824,0.02231711,0.003595396,0.00001530016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07141582,0.001591401,0.9233054,0.0003491123,0.00009796902,0.00005996361,0.00004423641,0.0003016528,0.002834422],"genre_scores_gemma":[0.8445312,0.001950297,0.1490943,0.0001169031,0.0001356667,0.000136318,0.00008795009,0.00005558349,0.003891714],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001179274,"threshold_uncertainty_score":0.006236672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0178336393231439,"score_gpt":0.2441087631620206,"score_spread":0.2262751238388767,"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."}}