{"id":"W2142617513","doi":"10.1145/1806338.1806370","title":"An intelligent agent for fault reconnaissance in sensor networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Wireless sensor network; Key distribution in wireless sensor networks; Computer science; Testbed; Mobile wireless sensor network; Sensor node; Computer network; Node (physics); Overhead (engineering); Fault (geology); Visual sensor network; Correctness; Reliability (semiconductor); Real-time computing; Distributed computing; Fault tolerance; Embedded system; Wireless; Wireless network; Engineering","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":[0.0007677086,0.000638056,0.0007665784,0.0005110628,0.0006366455,0.0009693783,0.001150629,0.001057773,0.001840523],"category_scores_gemma":[0.002239314,0.0001715723,0.0004348607,0.0004087778,0.000569932,0.0009815104,0.0005540182,0.000901643,0.0004497649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004978803,"about_ca_system_score_gemma":0.0007331714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001566564,"about_ca_topic_score_gemma":0.002471958,"domain_scores_codex":[0.9995327,0.0001451154,0.00003694453,0.00006853503,0.0001845807,0.00003219751],"domain_scores_gemma":[0.9993518,0.0002935289,0.0000789944,0.00009289006,0.0001498007,0.0000330215],"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":[0.0003199644,0.0002163591,0.002600869,0.00039057,0.0001340771,0.0005713086,0.0002960676,0.5440539,0.01517097,0.1079228,0.008295335,0.3200278],"study_design_scores_gemma":[0.00003130412,0.00008678664,0.0001792534,0.00001550703,0.00003036203,0.0001331855,0.00001732002,0.9772158,0.004088313,0.009473016,0.00871584,0.00001333915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006479484,0.0004222954,0.9885887,0.0002436576,0.0000966872,0.0001101601,0.00003658524,0.001103169,0.002919223],"genre_scores_gemma":[0.4092763,0.0006172354,0.5828713,0.0002357245,0.0001254884,0.0002972499,0.0001555742,0.0001009879,0.006320158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001840523,"threshold_uncertainty_score":0.006157219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02448056438254298,"score_gpt":0.2783475155408648,"score_spread":0.2538669511583219,"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."}}