{"id":"W2153965103","doi":"10.1002/dac.2315","title":"DDRP: An efficient data‐driven routing protocol for wireless sensor networks with mobile sinks","year":2012,"lang":"en","type":"article","venue":"International Journal of Communication Systems","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Computer network; Routing protocol; Network packet; Wireless Routing Protocol; Wireless sensor network; Source routing; Distributed computing; Triangular routing; Sink (geography); Packet forwarding","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":[0.001079735,0.0004374295,0.0006641001,0.0007684409,0.0003891234,0.0007054402,0.001281609,0.0006621144,0.0007367883],"category_scores_gemma":[0.001676795,0.0003556868,0.0004004563,0.0005301499,0.0004862714,0.001206204,0.001166495,0.0007211074,0.0004148172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003287466,"about_ca_system_score_gemma":0.0005350456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003573633,"about_ca_topic_score_gemma":0.00050521,"domain_scores_codex":[0.9994236,0.0001782744,0.00006923841,0.00006509689,0.0002277207,0.00003615538],"domain_scores_gemma":[0.9994748,0.0001661443,0.00007985471,0.00009057107,0.0001510584,0.00003755758],"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":[0.0006905897,0.0002061443,0.001393467,0.001143513,0.000257688,0.0008316445,0.0005058442,0.1971311,0.1252265,0.06103051,0.02145036,0.5901325],"study_design_scores_gemma":[0.000226909,0.0005660784,0.0006351226,0.00008076689,0.0001201694,0.0009302859,0.00006688177,0.8430287,0.05119049,0.02054924,0.08246926,0.0001361775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01300854,0.001234471,0.9808121,0.0003427249,0.0002442061,0.0001972998,0.0001076675,0.002101761,0.001951164],"genre_scores_gemma":[0.4493748,0.002608471,0.5367816,0.0005123959,0.0001903763,0.0008772789,0.001009109,0.0003190257,0.008326796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001281609,"threshold_uncertainty_score":0.005710244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05096756473966507,"score_gpt":0.3443068581358728,"score_spread":0.2933392933962077,"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."}}