{"id":"W1975685733","doi":"10.1109/milcom.2007.4455331","title":"An Energy-Efficient MAC Protocol Exploiting the Tree Structure in Wireless Sensor Networks","year":2007,"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":"University of Victoria","funders":"","keywords":"Computer science; Computer network; Wireless sensor network; Scheduling (production processes); Tree structure; Schedule; Distributed computing; Tree (set theory); Efficient energy use; Sink (geography); Data structure; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001066872,0.0004012329,0.0003075948,0.000237317,0.0003280498,0.0003351881,0.001977073,0.0002451104,0.0000292662],"category_scores_gemma":[0.00001742858,0.0002742082,0.00009905843,0.001544072,0.0001288597,0.0002778612,0.0003413096,0.0005395195,0.000003775999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001405343,"about_ca_system_score_gemma":0.00005072372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000200932,"about_ca_topic_score_gemma":0.00173507,"domain_scores_codex":[0.9962512,0.0002917217,0.0006936361,0.0009042298,0.0006723297,0.001186926],"domain_scores_gemma":[0.9975913,0.0003870567,0.0002317784,0.001431181,0.0001239091,0.0002347946],"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":[0.00002812874,0.0001402326,0.0007285853,0.000004997231,0.00000676701,0.00007682655,0.0003214297,0.8597507,0.00121601,0.0672783,0.00008086448,0.07036716],"study_design_scores_gemma":[0.0006122385,0.00007796998,0.002064939,0.00004175751,0.000002645948,0.0000376762,0.0002419642,0.9887606,0.006639875,0.0001010793,0.001024859,0.0003944334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09982771,0.00001100889,0.8870819,0.0002117155,0.0003641227,0.008496599,7.105423e-7,0.0004374769,0.003568705],"genre_scores_gemma":[0.9794603,0.000001241095,0.01606581,0.00081454,0.0004360099,0.003001219,0.000004753406,0.00004357227,0.0001725361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8796326,"threshold_uncertainty_score":0.999971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00915943868873296,"score_gpt":0.2500373424280316,"score_spread":0.2408779037392987,"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."}}