{"id":"W346289610","doi":"10.1007/978-3-319-15916-4_7","title":"Bio-inspired Routing Strategies for Wireless Sensor Networks","year":2015,"lang":"en","type":"book-chapter","venue":"Intelligent systems reference library","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Swarm intelligence; Wireless sensor network; Distributed computing; Swarm behaviour; Variety (cybernetics); Routing (electronic design automation); Simple (philosophy); Wireless ad hoc network; Wireless; Artificial intelligence; Computer network; Data science; Human–computer interaction; Telecommunications; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001360269,0.0006333382,0.0003497012,0.0003473842,0.0001905423,0.000639489,0.0008538683,0.0006258034,0.002612574],"category_scores_gemma":[0.000309366,0.000224953,0.0002419233,0.0005410541,0.0004017044,0.0007701236,0.0004451346,0.0006801086,0.0008972398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003555823,"about_ca_system_score_gemma":0.0001933342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003559133,"about_ca_topic_score_gemma":0.0005499455,"domain_scores_codex":[0.9999328,0.00001294349,0.000003790911,0.00001078367,0.00003478841,0.000004867947],"domain_scores_gemma":[0.9999537,0.00001994779,0.000005769242,0.000006619871,0.00001084375,0.00000311872],"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":[0.00003221523,0.00005962746,0.0001025,0.0006043914,0.00005902435,0.00007968504,0.0001170853,0.2251151,0.0234827,0.2891007,0.02035478,0.4408923],"study_design_scores_gemma":[0.00001820197,0.00008152417,0.0002306784,0.0001842279,0.00003324366,0.0002334041,0.00005862236,0.5957519,0.006599471,0.2551069,0.1416665,0.00003532029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01066897,0.05697539,0.8284925,0.001505219,0.001633441,0.00006659777,0.0001244081,0.000655323,0.0998781],"genre_scores_gemma":[0.3677652,0.07517257,0.4066161,0.001090651,0.001056561,0.0003378322,0.0004411061,0.0003811553,0.1471387],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002612574,"threshold_uncertainty_score":0.008739889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07655066415417264,"score_gpt":0.2631713523344164,"score_spread":0.1866206881802437,"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."}}