{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003698667,0.0001717112,0.0002233369,0.00008103478,0.0003841573,0.0003830753,0.001375086,0.0001661167,0.000009734745],"category_scores_gemma":[0.00006079347,0.0001418161,0.00007489155,0.0001251063,0.00008485238,0.0005995389,0.0002721194,0.0001504361,0.00000736566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005570605,"about_ca_system_score_gemma":0.00003256926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004095423,"about_ca_topic_score_gemma":0.0001307143,"domain_scores_codex":[0.9985334,0.00005177634,0.0002817943,0.0004911076,0.0002108256,0.0004310747],"domain_scores_gemma":[0.9981344,0.000181709,0.0002775477,0.001208681,0.0001086868,0.00008896889],"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":[0.0001217442,0.0003426499,0.01245092,0.00003927856,0.00002610183,0.00007421894,0.0006599396,0.4347497,0.0004586479,0.2861262,0.004503745,0.2604469],"study_design_scores_gemma":[0.0008756379,0.00004725723,0.004720925,0.0002259871,0.000001458534,0.000002911956,0.000008947075,0.9916372,0.001306875,0.0002616139,0.0007057176,0.0002054532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.189062,0.0000921341,0.8036966,0.0007993648,0.001291986,0.0003354092,5.17537e-7,0.0001623535,0.004559611],"genre_scores_gemma":[0.9851441,0.00003895871,0.0132846,0.0002206784,0.0001140513,0.00003700829,0.000004596781,0.00001559642,0.001140368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7960821,"threshold_uncertainty_score":0.5783092,"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."}}