{"id":"W2898179042","doi":"10.1109/tcyb.2018.2875625","title":"A Hybrid Strategy for Target Search Using Static and Mobile Sensors","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001769918,0.0001903047,0.0001765119,0.0001477885,0.000302158,0.0001581497,0.0002998787,0.00007100691,0.00002039833],"category_scores_gemma":[0.000002685095,0.0001951511,0.00006381539,0.000249733,0.0002028826,0.0001453056,0.000004336719,0.0001866161,0.00001581017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007564799,"about_ca_system_score_gemma":0.0001008934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000474857,"about_ca_topic_score_gemma":0.00001300563,"domain_scores_codex":[0.9985136,0.00007804628,0.0002369275,0.0004573473,0.0002684748,0.0004456234],"domain_scores_gemma":[0.9989402,0.0002497883,0.00005122383,0.0004299886,0.0001782655,0.0001505442],"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":[0.00001725872,0.0001094004,0.000002421954,0.00001548942,0.00002362119,0.000005759719,0.0006523983,0.9855345,0.001195142,0.0005191703,0.00004950877,0.01187533],"study_design_scores_gemma":[0.0003645836,0.000552368,0.000008023027,0.00003021497,0.00001496078,0.0000341558,0.0000510036,0.9324365,0.06551655,0.0002580791,0.0005269593,0.000206549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.328417,0.00002198605,0.6707903,0.00003580027,0.0002915057,0.0002449736,0.00001913708,0.00009044459,0.00008889887],"genre_scores_gemma":[0.8670465,0.0000203845,0.1323146,0.00008075821,0.00007739918,0.00002410979,0.000001226121,0.00002740008,0.0004076993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5386295,"threshold_uncertainty_score":0.7958031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03240713374068288,"score_gpt":0.2852336016983457,"score_spread":0.2528264679576628,"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."}}