{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003897295,0.0002052822,0.0002296986,0.0001309071,0.00008669699,0.0001539429,0.0009264852,0.0001245002,0.00001326571],"category_scores_gemma":[0.00002407349,0.0001899299,0.00008446116,0.0004868825,0.00002920518,0.0003134573,0.00004796388,0.0001711654,0.00001257289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008060808,"about_ca_system_score_gemma":0.00001964519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001793497,"about_ca_topic_score_gemma":0.00008388845,"domain_scores_codex":[0.9981117,0.00008053169,0.0003913132,0.0006427955,0.0001795764,0.0005940876],"domain_scores_gemma":[0.9987739,0.000131234,0.00008705954,0.0007874931,0.00008270358,0.0001376372],"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":[0.0000124738,0.000145607,0.00008703778,0.000001868959,0.000002738477,0.0000110742,0.0001245978,0.8469111,0.0001347053,0.02998193,0.001220767,0.121366],"study_design_scores_gemma":[0.0002689497,0.0002508341,0.0009399462,0.00002964434,0.000001645243,0.000007242799,0.00002777146,0.9920984,0.001676467,0.000487886,0.003945495,0.0002657186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02721343,0.0002064035,0.9686289,0.001014772,0.0005863018,0.00033141,4.800165e-7,0.0002935563,0.001724711],"genre_scores_gemma":[0.8539525,0.00006527325,0.1429869,0.002307411,0.0001738361,0.00002026131,0.000005910983,0.00001209,0.0004757928],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8267391,"threshold_uncertainty_score":0.7745115,"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."}}