{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008487045,0.001226219,0.002019624,0.001570053,0.0008979509,0.001696703,0.002576653,0.001543089,0.0004405972],"category_scores_gemma":[0.0318327,0.001177899,0.0009004458,0.002197337,0.001798141,0.006019221,0.002043698,0.002153791,0.0001619209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001463098,"about_ca_system_score_gemma":0.0009684644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004074985,"about_ca_topic_score_gemma":0.002781336,"domain_scores_codex":[0.9969536,0.001313453,0.0002049376,0.0005314857,0.0008206501,0.0001759203],"domain_scores_gemma":[0.9872764,0.009721595,0.00113001,0.0009881301,0.0007428527,0.0001410038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00012359,0.00003798291,0.002365249,0.00008300433,0.00007703289,0.0001159619,0.0001245141,0.9461267,0.001029093,0.01234283,0.0004499549,0.03712414],"study_design_scores_gemma":[0.000005648302,0.00001175437,0.0002826561,0.000005607651,0.000009011236,0.00002486384,0.00001169217,0.9864199,0.0005729361,0.01246403,0.0001848391,0.000007123569],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01249404,0.0004457723,0.9862281,0.0002292725,0.00002496642,0.00002820225,0.00003700704,0.0002067537,0.0003058155],"genre_scores_gemma":[0.7089376,0.00158259,0.2864914,0.0002971585,0.0002420191,0.0002519012,0.0004238618,0.0001775542,0.001595944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008487045,"threshold_uncertainty_score":0.04488432,"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."}}