{"id":"W2140182727","doi":"10.1109/pacrim.2005.1517285","title":"An energy-aware spanning tree algorithm for data aggregation in wireless sensor networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Wireless sensor network; Node (physics); Spanning tree; Computer network; Tree (set theory); Data aggregator; Routing protocol; Energy (signal processing); Span (engineering); Residual; Distributed computing; Algorithm; Wireless; Routing (electronic design automation); Real-time computing; Mathematics; Engineering; Telecommunications; Statistics","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":[0.0005240177,0.000280534,0.0002921008,0.0002129535,0.0001633725,0.0002787341,0.002209503,0.0002031283,0.000008166528],"category_scores_gemma":[0.000009731024,0.0002771144,0.00005523439,0.0006932319,0.00004524475,0.001531035,0.0004076899,0.0001927631,0.00000467257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001032451,"about_ca_system_score_gemma":0.00005384022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002007249,"about_ca_topic_score_gemma":0.00155751,"domain_scores_codex":[0.9972965,0.0001283858,0.000472004,0.001074655,0.0003306262,0.000697793],"domain_scores_gemma":[0.997402,0.0002449002,0.0001605273,0.001902991,0.0001155088,0.0001740605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003694506,0.0000886281,0.0001042419,0.000001720527,0.000006156337,0.000007239356,0.00004977862,0.3152972,0.00002808351,0.004118315,0.0005504228,0.6797445],"study_design_scores_gemma":[0.0006368984,0.00006296345,0.0001223327,0.00004663771,0.000005672695,0.00001251111,0.0000345488,0.9932995,0.0007628416,0.0000416649,0.004605589,0.0003687753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003856593,0.0001575313,0.9940622,0.0004718077,0.0004196791,0.0001807493,0.000007035866,0.0004037181,0.0004406936],"genre_scores_gemma":[0.5058888,0.00005973132,0.4919419,0.0005740578,0.0007746901,0.00002939309,0.0002746734,0.0000370173,0.0004197387],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6793757,"threshold_uncertainty_score":0.9999681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0236597866728387,"score_gpt":0.2678445379456811,"score_spread":0.2441847512728424,"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."}}