{"id":"W1901360455","doi":"10.1109/wirles.2005.1549541","title":"Cross-layer organization of wireless sensor networks using sense-sleep trees","year":2005,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wireless sensor network; Computer science; Integer programming; Distributed computing; Computer network; Tree (set theory); Linear programming; Algorithm","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":[0.0002277244,0.0002438061,0.0002972503,0.0001472997,0.0001816551,0.0001904867,0.0005502735,0.0001948086,0.00009597747],"category_scores_gemma":[0.00002954315,0.0002282008,0.00007823408,0.001317382,0.0001100387,0.000541516,0.0002747092,0.000166958,0.00002266201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008562188,"about_ca_system_score_gemma":0.00003750472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006245013,"about_ca_topic_score_gemma":0.00004811649,"domain_scores_codex":[0.997991,0.00009578624,0.0005136932,0.0005192758,0.0003986116,0.0004816184],"domain_scores_gemma":[0.9983019,0.0001424154,0.0002429818,0.0007618815,0.000428876,0.0001219359],"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":[0.000003167067,0.00007922559,0.006935106,0.000004327804,0.00001839319,0.000008193966,0.0001491739,0.9713472,0.007358185,0.006869254,0.00007131892,0.007156451],"study_design_scores_gemma":[0.0003106976,0.00001888432,0.002732907,0.00002154109,0.000009156466,0.00004441856,0.0000151363,0.9582257,0.0381276,0.000004549345,0.0002302348,0.0002592065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4678457,0.00003437798,0.5308878,0.00009132587,0.0002301699,0.00006394724,4.656311e-7,0.0002040427,0.0006421158],"genre_scores_gemma":[0.9031165,0.0000178861,0.0956716,0.0002576876,0.0003252884,8.795975e-7,0.000005132151,0.00003659389,0.0005684862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4352707,"threshold_uncertainty_score":0.9305757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01545634248561225,"score_gpt":0.2488810641684263,"score_spread":0.2334247216828141,"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."}}