{"id":"W2935974895","doi":"10.3390/s19081887","title":"A Practical Neighbor Discovery Framework for Wireless Sensor Networks","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Neighbor Discovery Protocol; Latency (audio); Computer science; Spear; Wireless sensor network; Service discovery; Boosting (machine learning); Computer network; Wireless; Telecommunications; World Wide Web; Machine learning; The Internet","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":[0.0003985298,0.0004101598,0.0005020207,0.0001358944,0.0001870213,0.0005106999,0.0008401421,0.0004150735,0.00002396222],"category_scores_gemma":[0.0002132839,0.0003773017,0.0002907058,0.0006896898,0.00009931882,0.000659415,0.0003012816,0.0006120635,0.0001747805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009548321,"about_ca_system_score_gemma":0.00009485841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001371623,"about_ca_topic_score_gemma":0.000005503539,"domain_scores_codex":[0.9966613,0.0001839978,0.0004742998,0.00108466,0.0005206279,0.001075109],"domain_scores_gemma":[0.9954932,0.00234502,0.0002664231,0.001488317,0.0001664336,0.0002406161],"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":[0.00007557506,0.0001297167,0.0007116757,0.00002643386,0.00005740343,0.00004045066,0.0001438226,0.45933,0.0001321077,0.5361319,0.001148992,0.002072038],"study_design_scores_gemma":[0.0005446611,0.0001683201,0.0003588616,0.0001017689,0.00001976155,0.00005475745,0.00009343157,0.9909422,0.000652121,0.001413257,0.005089441,0.0005614493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2586759,0.00004654695,0.7352836,0.001905898,0.002238193,0.0005598035,0.000005229879,0.000365655,0.0009191778],"genre_scores_gemma":[0.7305973,0.00003640413,0.2656948,0.0008974931,0.000628148,0.00003493116,0.00001106947,0.0000690637,0.002030854],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5347186,"threshold_uncertainty_score":0.9998679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01580383002708546,"score_gpt":0.2735053763853939,"score_spread":0.2577015463583084,"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."}}