{"id":"W2162230726","doi":"10.1109/ssdbm.2006.21","title":"Efficient Data Harvesting for Tracing Phenomena in Sensor Networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Wireless sensor network; Computer science; Overhead (engineering); Publication; Visual sensor network; Computer network; Tracing; Distributed computing; Key distribution in wireless sensor networks; Routing (electronic design automation); Object (grammar); Real-time computing; Wireless network; Wireless; 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":[0.0008129049,0.0002142534,0.0002437169,0.0001591271,0.000159178,0.0002514901,0.001534447,0.00009092383,0.000004167658],"category_scores_gemma":[0.00005674037,0.0002031471,0.00004811669,0.0007205862,0.00004026152,0.0002170589,0.0005427376,0.0001788006,0.00000635563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007580072,"about_ca_system_score_gemma":0.00003229821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003091927,"about_ca_topic_score_gemma":0.0004162228,"domain_scores_codex":[0.9975628,0.00006978737,0.0004875485,0.0008870973,0.0002490616,0.0007437054],"domain_scores_gemma":[0.9978595,0.0005526855,0.0001277778,0.001325066,0.00005908411,0.00007590668],"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":[0.000003983524,0.00009537779,0.0002598068,0.000007606526,0.000003061684,0.000009776045,0.00003318634,0.9712951,0.0000871571,0.01533094,0.00047524,0.01239876],"study_design_scores_gemma":[0.0005012266,0.00002108005,0.0008054443,0.00004303797,0.000003578438,0.000008294595,0.00002047557,0.9970776,0.000102873,0.00005004324,0.001102892,0.0002634283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02704737,0.0002001436,0.9652062,0.0002549551,0.0004418894,0.0003062144,0.000003614743,0.0003202512,0.006219368],"genre_scores_gemma":[0.7192791,0.000001888602,0.2789834,0.0001632772,0.0005496099,0.00001773008,0.0000413581,0.00002400974,0.0009396077],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6922317,"threshold_uncertainty_score":0.8284097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02714971372680825,"score_gpt":0.2449326873920906,"score_spread":0.2177829736652823,"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."}}