{"id":"W2108798476","doi":"10.1109/glocom.2008.ecp.33","title":"Weaving a Proper Net to Catch Large Objects","year":2008,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Weaving; Wireless sensor network; Computer science; Cover (algebra); Object (grammar); Net (polyhedron); Point (geometry); Field (mathematics); Object detection; Real-time computing; Artificial intelligence; Computer network; Engineering; Mathematics; Pattern recognition (psychology); Geometry","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":[0.0001831175,0.0001393729,0.000140835,0.00009230876,0.0002635368,0.00007152322,0.0007909133,0.00005676524,0.00004561745],"category_scores_gemma":[0.00003254127,0.0001130015,0.0000451638,0.0006621312,0.00001927631,0.0002212194,0.0004247354,0.000149155,0.0005233136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003997297,"about_ca_system_score_gemma":0.00005770161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000957558,"about_ca_topic_score_gemma":0.0001255307,"domain_scores_codex":[0.99848,0.00006018371,0.0001658383,0.0004494595,0.0003116772,0.0005328155],"domain_scores_gemma":[0.9990076,0.00005263104,0.00002987928,0.0006504914,0.00007512309,0.000184304],"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":[0.00003613834,0.001403247,0.02020736,0.00004124261,0.00008685177,0.001352505,0.03418472,0.4855693,0.01031612,0.1951442,0.2277236,0.02393477],"study_design_scores_gemma":[0.001037482,0.0003104737,0.01085893,0.00008844706,0.000006026301,0.0003751234,0.0001839344,0.8616378,0.01871993,0.00008169894,0.1054357,0.001264415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2660252,0.00006549766,0.6922205,0.001129663,0.0006413215,0.0002162544,4.721149e-7,0.000653971,0.0390471],"genre_scores_gemma":[0.9236972,0.00000718968,0.06536885,0.00208236,0.0001086163,0.00001398016,8.799524e-7,0.00001348255,0.008707402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.657672,"threshold_uncertainty_score":0.6726313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443928913920114,"score_gpt":0.217119028619363,"score_spread":0.2026797394801619,"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."}}