{"id":"W70033783","doi":"10.1007/978-3-642-01341-6_8","title":"Markov Decision Process-Based Resource and Information Management for Sensor Networks","year":2009,"lang":"en","type":"book-chapter","venue":"Signals and communication technology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Department of National Defence; Defence Research and Development Canada; General Dynamics (Canada); McMaster University","funders":"","keywords":"Computer science; Sensor fusion; Markov decision process; Wireless sensor network; Process (computing); Markov process; Real-time computing; Distributed computing; Data mining; Artificial intelligence; Computer network","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":[0.001466414,0.0008720202,0.001345525,0.0003828209,0.000505603,0.001262037,0.001839527,0.00106946,0.003448011],"category_scores_gemma":[0.002229931,0.0005483441,0.0007462789,0.001005598,0.001056486,0.001632567,0.0009660996,0.001863411,0.0004821971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001887008,"about_ca_system_score_gemma":0.001710377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007958365,"about_ca_topic_score_gemma":0.007749711,"domain_scores_codex":[0.9992936,0.0002118492,0.00003731609,0.0001284299,0.000226986,0.0001018855],"domain_scores_gemma":[0.9987545,0.0009151634,0.00005935062,0.00007402375,0.0001521806,0.00004486142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007002798,0.00004780975,0.0001565464,0.00005898837,0.00004360311,0.00004106569,0.0000402638,0.8887829,0.0009878636,0.07710586,0.002805962,0.02985916],"study_design_scores_gemma":[0.000005306124,0.00001009805,0.00003024531,0.000003366546,0.000005428542,0.000005638867,0.000002696524,0.9676968,0.0001774208,0.03165312,0.0004049148,0.000004775737],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.005831332,0.0008509849,0.9874411,0.0003601644,0.0001082272,0.00004286608,0.0001048182,0.000355779,0.004904797],"genre_scores_gemma":[0.7181119,0.002406529,0.2623844,0.0003291192,0.0003166703,0.000366738,0.000411545,0.0001937019,0.01547936],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.007958365,"threshold_uncertainty_score":0.01582408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005268690409078372,"score_gpt":0.2267730927916833,"score_spread":0.2215044023826049,"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."}}