{"id":"W2606802803","doi":"10.1109/icin.2017.7899422","title":"Concealed regression for aggregation in low power wireless networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Lawrence Berkeley National Laboratory","keywords":"Computer science; Computer network; Data aggregator; Encryption; Vulnerability (computing); Bandwidth (computing); Node (physics); Wireless network; Routing (electronic design automation); Distributed computing; Wireless; Transmission (telecommunications); Wireless sensor network; Computer security; Telecommunications","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":[0.002203083,0.0007550566,0.0009061365,0.0006269839,0.0004359837,0.0008439429,0.0008592653,0.0007656264,0.001206083],"category_scores_gemma":[0.009560979,0.0003338859,0.000542017,0.0008045354,0.0009909492,0.001360026,0.0009499708,0.001544762,0.0003839032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008199094,"about_ca_system_score_gemma":0.0006333083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002127215,"about_ca_topic_score_gemma":0.001606169,"domain_scores_codex":[0.9989955,0.0004129842,0.00003616672,0.0001720531,0.0003079523,0.00007526787],"domain_scores_gemma":[0.9962389,0.002796354,0.0002984309,0.0003017495,0.0003123365,0.00005218332],"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":[0.0001834364,0.00006031668,0.001096127,0.0001151904,0.00007469563,0.0001036699,0.0001208444,0.8352012,0.006698174,0.05283647,0.001287046,0.1022228],"study_design_scores_gemma":[0.000002839937,0.00001709376,0.00005878841,0.000003253952,0.00000356225,0.000008798903,0.000002789051,0.9953237,0.0006018109,0.003646912,0.0003277848,0.000002698303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007696823,0.0003906626,0.9908757,0.0001504729,0.00003928371,0.00001420001,0.00001449482,0.0001951374,0.000623253],"genre_scores_gemma":[0.6603523,0.001512946,0.3290326,0.0001469508,0.0002399223,0.0001483284,0.0001523576,0.0001947328,0.008219995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002203083,"threshold_uncertainty_score":0.0116511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01344412790107637,"score_gpt":0.2644934072275016,"score_spread":0.2510492793264252,"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."}}