{"id":"W1586378172","doi":"10.1007/978-3-642-17878-8_66","title":"Dynamic Sink Placement in Wireless Sensor Networks","year":2010,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sink (geography); Wireless sensor network; Computer science; Energy consumption; Computer network; Wireless; Dissemination; Key distribution in wireless sensor networks; Real-time computing; Distributed computing; Wireless network; Engineering; Telecommunications; Electrical 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":[0.001285841,0.0003390588,0.0003633276,0.00140813,0.0004294486,0.0007080432,0.004490948,0.0003263766,0.000007976569],"category_scores_gemma":[0.00002117094,0.0003571207,0.00005351483,0.0007837625,0.001050027,0.00380316,0.003221261,0.001300326,0.00003404655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002835506,"about_ca_system_score_gemma":0.0002397732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001646073,"about_ca_topic_score_gemma":0.0001404994,"domain_scores_codex":[0.9974745,0.00005210108,0.0009704534,0.0004687928,0.0005731102,0.0004610492],"domain_scores_gemma":[0.995788,0.0003316867,0.0004346032,0.003006449,0.0002958521,0.0001433995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005408775,0.00006203027,0.0001005261,0.00002626341,0.000006865747,0.000003524987,0.001447025,0.199212,0.00001711341,0.6115933,0.00007179628,0.1874541],"study_design_scores_gemma":[0.0003083649,0.00003194551,0.0005753106,0.000222843,0.000002390537,0.000022828,0.00001026672,0.97522,0.000005017046,0.0005960599,0.02263057,0.0003744061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006447894,0.0002946396,0.8911654,0.0009490202,0.0009393863,0.0005483688,0.000005205969,0.0001633929,0.1052898],"genre_scores_gemma":[0.3902857,0.008584074,0.5950133,0.002432063,0.00009250076,0.00008891393,0.0001855475,0.00004805968,0.003269871],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.776008,"threshold_uncertainty_score":0.9998881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01550545349585276,"score_gpt":0.2555979927719363,"score_spread":0.2400925392760835,"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."}}