{"id":"W2491654193","doi":"10.1109/smartcomp.2016.7501683","title":"Client-Catered Control of Engineered Spaces with Software-Defined Sensors and Actuators","year":2016,"lang":"en","type":"article","venue":"","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geomembrane Technologies (Canada)","funders":"Washington State University; National Science Foundation","keywords":"Computer science; Actuator; Software; Wireless sensor network; Node (physics); Control (management); Event (particle physics); Routing (electronic design automation); Software-defined networking; Embedded system; Real-time computing; Computer network; Engineering; Operating system; Artificial intelligence","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":[0.00005475443,0.0001278611,0.0001838119,0.00006685914,0.0000204225,0.00001104631,0.00006840617,0.00005795337,0.00005376555],"category_scores_gemma":[0.00004091432,0.00006978642,0.00002419622,0.00008196034,0.00008997159,0.0000970355,0.00001010615,0.00004263262,0.00001149682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001061159,"about_ca_system_score_gemma":0.00000842928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000099463,"about_ca_topic_score_gemma":0.00001484302,"domain_scores_codex":[0.9994224,0.000009805325,0.0001341684,0.0001266286,0.0001155515,0.0001913922],"domain_scores_gemma":[0.9995677,0.0001570887,0.00002079576,0.0001455882,0.00002763151,0.00008113011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001340783,0.0003671669,0.325033,0.001615099,0.001841092,0.0001001396,0.008233269,0.03918034,0.5359158,0.01578848,0.005116807,0.06546801],"study_design_scores_gemma":[0.02451798,0.001736632,0.1523198,0.001153123,0.000367766,0.0002146972,0.001587848,0.03606893,0.7562325,0.0009296262,0.02151248,0.003358671],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9167376,0.0001171158,0.08237373,0.0001145625,0.0001135434,0.0001098831,0.00001341375,0.0002137501,0.0002063788],"genre_scores_gemma":[0.997931,0.00007893728,0.001789451,0.0000132709,0.00002783514,0.000005302118,5.984745e-7,0.00001743836,0.0001361934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2203167,"threshold_uncertainty_score":0.2845808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003797427095720931,"score_gpt":0.1657421679484991,"score_spread":0.1619447408527782,"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."}}