{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000237565,0.0002283511,0.0002540968,0.0002621822,0.0001818884,0.00003556726,0.0003050183,0.0006640473,0.000008983478],"category_scores_gemma":[0.00001531771,0.0002339589,0.00007385596,0.00006361749,0.0001651161,0.000007434769,0.0001877269,0.000158711,0.000001362354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001314336,"about_ca_system_score_gemma":0.00001876077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.654165e-7,"about_ca_topic_score_gemma":0.000004876526,"domain_scores_codex":[0.9991027,0.00001956709,0.0003450848,0.0002771551,0.00009440054,0.0001611112],"domain_scores_gemma":[0.9986364,0.00003048903,0.0003009356,0.0008369806,0.0001510641,0.00004417474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000205986,0.00002018037,0.00004038458,0.00009900419,0.0002210478,5.350856e-7,0.00001003331,0.002506388,0.000233695,0.01269273,0.002421387,0.9815486],"study_design_scores_gemma":[0.001000477,0.0004057445,0.00006916642,0.0002511228,0.0002907597,0.00001371281,0.00009031196,0.007113018,0.0009641658,0.03885357,0.9504142,0.000533703],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02394804,0.1882273,0.5598848,0.01303527,0.0001455201,0.008920624,0.0002065451,0.0007059334,0.2049259],"genre_scores_gemma":[0.8194246,0.0493324,0.07879895,0.003605058,0.0002215514,0.0005731151,0.005012686,0.0001905148,0.04284116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9810149,"threshold_uncertainty_score":0.9540567,"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."}}