{"id":"W2117025565","doi":"10.1155/2010/985624","title":"Fault Reconnaissance Agent for Sensor Networks","year":2010,"lang":"en","type":"article","venue":"Mobile Information Systems","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Wireless sensor network; Testbed; Correctness; Scalability; Distributed computing; Overhead (engineering); Inference; Key distribution in wireless sensor networks; Fault (geology); Fault tolerance; Real-time computing; Set (abstract data type); Embedded system; Computer network; Wireless; Artificial intelligence; Wireless network; Algorithm","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":[0.0004947697,0.0005172673,0.0005306327,0.0003491461,0.0004267732,0.0008364305,0.000786584,0.0008220315,0.002031925],"category_scores_gemma":[0.001584791,0.0001494037,0.000300801,0.0004083381,0.0004781065,0.0008209606,0.000466022,0.0008942504,0.0005713666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004627255,"about_ca_system_score_gemma":0.0006452454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00137255,"about_ca_topic_score_gemma":0.00154022,"domain_scores_codex":[0.9995778,0.0001362212,0.00003443501,0.00007289698,0.0001517077,0.00002690732],"domain_scores_gemma":[0.9995686,0.0001684398,0.00006559832,0.00007345977,0.000105201,0.0000185541],"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":[0.0002194283,0.0001415478,0.001464172,0.0007338927,0.0001351847,0.0005192726,0.0003112835,0.3793524,0.02678449,0.1543953,0.01405926,0.4218838],"study_design_scores_gemma":[0.00002880753,0.00008074566,0.0002888281,0.00002723922,0.00002500489,0.0002073935,0.0000241538,0.9446546,0.00771194,0.02371581,0.02321887,0.00001660517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005414559,0.001030809,0.9895201,0.0002747264,0.00009319841,0.00007904314,0.00004748158,0.001166199,0.002373899],"genre_scores_gemma":[0.3422496,0.001320682,0.646633,0.0002110687,0.0001691641,0.0003424864,0.0003715161,0.0001449682,0.008557496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002031925,"threshold_uncertainty_score":0.006797433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01071310528449153,"score_gpt":0.2303968786077313,"score_spread":0.2196837733232397,"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."}}