{"id":"W1999954621","doi":"10.1109/tcyb.2014.2328659","title":"Semi-Flocking Algorithm for Motion Control of Mobile Sensors in Large-Scale Surveillance Systems","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Flocking (texture); Computer science; Algorithm; Wireless sensor network; Distributed computing; Real-time computing; Artificial intelligence; Computer network","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":[],"consensus_categories":[],"category_scores_codex":[0.0006089512,0.0001962426,0.0003690356,0.000196785,0.0000958457,0.00005226053,0.0003771799,0.0001639571,0.000002519511],"category_scores_gemma":[0.000007066617,0.0002078262,0.000122612,0.0004325073,0.00004617461,0.0000986431,0.000002501593,0.0002077868,0.000005530927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007880379,"about_ca_system_score_gemma":0.00001991466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000598726,"about_ca_topic_score_gemma":0.000126956,"domain_scores_codex":[0.9981391,0.0002086069,0.0004914934,0.0004281857,0.0003158099,0.0004167762],"domain_scores_gemma":[0.9984573,0.0005779875,0.0001751069,0.0005385375,0.000171089,0.00008002081],"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":[0.000007606191,0.0002006506,0.00008058065,0.00002300761,0.00001346637,8.456788e-7,0.0002823111,0.9636296,0.0003264691,0.0002922921,0.00001073468,0.03513245],"study_design_scores_gemma":[0.001235476,0.0001850529,0.0001387294,0.00006867437,0.000009103375,0.000005792187,0.00005598391,0.9922154,0.005268542,0.00002061768,0.0005990737,0.0001975337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03901726,0.0000752595,0.9588705,0.00003150323,0.001267889,0.0004723516,0.00004759345,0.0001171415,0.0001004999],"genre_scores_gemma":[0.9798363,0.00003471294,0.01972333,0.00003280972,0.00007247211,0.0001019983,0.000002973573,0.00002713773,0.0001682337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9408191,"threshold_uncertainty_score":0.8474905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006583218962908225,"score_gpt":0.2159047349912012,"score_spread":0.209321516028293,"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."}}