{"id":"W2017811850","doi":"10.1007/s10586-009-0090-2","title":"Using ant-based agents for congestion control in ad-hoc wireless sensor networks","year":2009,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Canadian Centre for Applied Research in Cancer Control","keywords":"Computer science; ANT; Wireless ad hoc network; Computer network; Network congestion; Wireless sensor network; Wireless; Telecommunications; Network packet","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":[0.001001749,0.000415256,0.0005551258,0.0005885519,0.0006794792,0.0009956638,0.001057902,0.0006383774,0.0005688694],"category_scores_gemma":[0.002866912,0.0003115536,0.0002094421,0.0006104818,0.0006009684,0.001018152,0.0005980344,0.0006217474,0.0001310183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003182713,"about_ca_system_score_gemma":0.0004767755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001997188,"about_ca_topic_score_gemma":0.00260909,"domain_scores_codex":[0.9995323,0.0001941102,0.00003574685,0.00004927361,0.0001565407,0.0000320061],"domain_scores_gemma":[0.9988014,0.0005929118,0.0001046507,0.0001074058,0.0003222199,0.00007152952],"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":[0.0005322239,0.000227529,0.002055081,0.0002238072,0.0001718341,0.0001825731,0.0002788983,0.7407868,0.02879088,0.02570045,0.003149343,0.1979005],"study_design_scores_gemma":[0.00002012095,0.00004904427,0.0001087385,0.00000362225,0.00002197288,0.00002800739,0.00001400138,0.9914863,0.003565298,0.00341032,0.001284565,0.000007940384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08566348,0.001423276,0.9073095,0.0004110288,0.000352396,0.0001015125,0.00002409033,0.0009671258,0.003747585],"genre_scores_gemma":[0.8913207,0.0005438527,0.105395,0.00007726334,0.00008094093,0.00006171807,0.00002755337,0.0000806746,0.002412158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001997188,"threshold_uncertainty_score":0.00529778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02928262745674676,"score_gpt":0.2800714539968702,"score_spread":0.2507888265401234,"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."}}