{"id":"W2003314357","doi":"10.1145/1143549.1143823","title":"Integrating wireless EEGs into medical sensor networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Wireless sensor network; Computer science; Key distribution in wireless sensor networks; Wireless; Mobile wireless sensor network; Real-time computing; Wireless network; Computer network; 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":[0.0005174194,0.0004822669,0.0003121143,0.0003306794,0.0001465903,0.0007430487,0.0005771701,0.0005128884,0.00326149],"category_scores_gemma":[0.001956289,0.0002168029,0.0001848071,0.0005025879,0.0002643016,0.001445615,0.000719892,0.0004593966,0.000965141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002334586,"about_ca_system_score_gemma":0.0002424306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005205679,"about_ca_topic_score_gemma":0.0006494459,"domain_scores_codex":[0.9996195,0.0001280655,0.00002587863,0.00006529083,0.0001348001,0.00002654053],"domain_scores_gemma":[0.9994917,0.000228749,0.00003566802,0.00007036501,0.0001456365,0.00002795099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002056724,0.00008931389,0.002622814,0.0004479479,0.00005924477,0.0003368158,0.0001206258,0.05106179,0.02947871,0.04151724,0.0106844,0.8633755],"study_design_scores_gemma":[0.00005625728,0.0003969813,0.002962411,0.0001505754,0.00009576962,0.0007774458,0.0001923508,0.7253352,0.01928742,0.08288957,0.1678079,0.00004810655],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01379374,0.005905662,0.967366,0.001821965,0.0005654497,0.0001004931,0.00005910681,0.0007580337,0.009629422],"genre_scores_gemma":[0.4054369,0.01939877,0.540674,0.00117276,0.001540774,0.0003049483,0.0004110456,0.0001466614,0.03091421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00326149,"threshold_uncertainty_score":0.01091075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004325107697092241,"score_gpt":0.2122732825897573,"score_spread":0.207948174892665,"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."}}