{"id":"W2159105687","doi":"10.1145/984622.984680","title":"Loss inference in wireless sensor networks based on data aggregation","year":2004,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Wireless sensor network; Inference; Wireline; Computer science; Node (physics); Key distribution in wireless sensor networks; Sensor node; Computer network; Wireless; Wireless network; Data mining; Artificial intelligence; Engineering; Telecommunications","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":[0.0004035754,0.0002260204,0.0002094629,0.0002013978,0.00008846207,0.0001815377,0.002009519,0.0001497724,0.00001017399],"category_scores_gemma":[0.00008746891,0.0002085683,0.00003308279,0.001054382,0.00006851717,0.0005325788,0.0004233665,0.000334203,0.00004753342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001319817,"about_ca_system_score_gemma":0.0001196178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002070314,"about_ca_topic_score_gemma":0.0004567424,"domain_scores_codex":[0.9977978,0.0001063798,0.0003372379,0.0008537644,0.0004351205,0.0004697239],"domain_scores_gemma":[0.9971442,0.0004207965,0.0001101384,0.002143947,0.00006592657,0.000114972],"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":[0.000008615541,0.0001340276,0.001390164,0.000003261461,0.00000221392,0.00006285787,0.00002308049,0.9399946,0.00001720451,0.04767448,0.00003515188,0.01065432],"study_design_scores_gemma":[0.0007718016,0.00005613194,0.001582508,0.0001397381,0.000001744053,0.000003168269,0.000005226484,0.9962881,0.0005767408,0.0002195388,0.00009353634,0.0002618029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05601245,0.00001756895,0.940008,0.001114953,0.0004111367,0.0001545731,0.00000198423,0.0002856786,0.001993618],"genre_scores_gemma":[0.9579459,0.00001775848,0.04071933,0.001089977,0.00009413381,0.00000755671,0.00004922226,0.00001608253,0.00006002214],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9019335,"threshold_uncertainty_score":0.8505167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02501533658855394,"score_gpt":0.2605907871498986,"score_spread":0.2355754505613447,"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."}}