{"id":"W2116212334","doi":"","title":"A coverage dominance approach for sensor deployment optimization","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Terrain; Software deployment; Wireless sensor network; Computer science; Robustness (evolution); Heuristic; Dominance (genetics); Wireless; Real-time computing; Distributed computing; Computer network; Artificial intelligence; Telecommunications; Geography; Cartography","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":[0.00128106,0.001329864,0.001098774,0.001086744,0.000452023,0.0007967998,0.00110795,0.001041892,0.001927575],"category_scores_gemma":[0.003124715,0.0004746219,0.0008159334,0.001176888,0.0005544403,0.0008467766,0.00104207,0.0008437511,0.0003157372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008357172,"about_ca_system_score_gemma":0.0008387272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003184031,"about_ca_topic_score_gemma":0.002280178,"domain_scores_codex":[0.9991185,0.0004057322,0.00002616063,0.00007604763,0.0002820994,0.00009129354],"domain_scores_gemma":[0.9989503,0.0007020533,0.00007104563,0.00003575254,0.0001765339,0.00006432195],"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":[0.0000292417,0.00003749213,0.0002954073,0.0001082646,0.00006275417,0.00008634336,0.00005247869,0.9407975,0.001782342,0.02160304,0.00277116,0.03237408],"study_design_scores_gemma":[0.000008076233,0.00004102979,0.00006360361,0.00001039225,0.000008166635,0.000023752,0.000008726663,0.9903806,0.0002013463,0.007716446,0.001532575,0.000005181048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004740173,0.0008835684,0.9898788,0.000226237,0.00006027484,0.00003816358,0.00004917979,0.00006585567,0.004057819],"genre_scores_gemma":[0.624849,0.003393408,0.355309,0.0006131973,0.0004994619,0.0006422077,0.000371332,0.0001938027,0.01412871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003184031,"threshold_uncertainty_score":0.006774962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03650896484623942,"score_gpt":0.2431458641925375,"score_spread":0.2066368993462981,"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."}}